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Quantum Information

23 missions · 15 completed

The study of information encoded in the states of quantum systems, where the qubit, superposition, and entanglement replace the classical bit and measurement is inherently probabilistic. Governed by the constraints of quantum mechanics, it underpins quantum computing and cryptography and the theory of optimal quantum measurements.

Missions

Open8Completed15All23
Algebra·Captain: wenxinzhang

Existence of complete sets of mutually unbiased basesOpen Problem

Motivation

Two orthonormal bases of C^d are mutually unbiased when every transition amplitude has squared modulus 1/d. At most d+1 such bases can coexist, and complete families are known in prime-power dimensions through finite-field constructions. Dimension six is the smallest famous composite case where existence of the complete seven-base family remains unknown.

This mission turns CUHK-Shenzhen AI Math Problem 16, Existence of complete sets of mutually unbiased bases, into an auditable Lean campaign. The objective is not merely to transcribe notation: it is to expose the mathematical model, the capstone, and a smaller attack surface as separate artifacts that other formalizers can inspect and reuse.

Setting

Construct seven 6 by 6 unitary-column matrices whose every distinct pair has all transition amplitudes of squared modulus 1/6. The baseline milestone constructs three pairwise mutually unbiased bases, a known lower bound that tests all matrix conventions.

Significance

Solving this target would settle the precise finite or analytic core represented by the Lean statement and would create reusable infrastructure in quantum information theory, mutually unbiased bases, finite fields, Hilbert spaces. Even a rigorous disproof is valuable: several entries in this collection deliberately ask whether an attractive extrapolation is true, and Lean forces a counterexample to satisfy every side condition. The mission therefore treats theorem proving and model criticism as equally legitimate research outcomes.

Difficulty

The equations are a large coupled system of polynomial equalities over complex phases, modulo substantial gauge symmetry. Numerical near-solutions do not certify exact existence, while nonexistence would require a global obstruction beyond currently known bounds. Dimension six lacks the finite-field structure that supplies complete prime-power constructions.

Suggested attack route

Formalize standard gauge reductions: fix the first basis to the identity and dephase transition Hadamard matrices. Verify a three-basis tensor-product construction. Then encode additional bases through complex Hadamard matrices and study algebraic constraints, Gröbner-style eliminations, semidefinite bounds, or exact certificates. Computational searches may guide conjectures, but uploaded proofs must convert numerical evidence to exact algebraic identities or certified inequalities.

Formalization scope

The Lean target is exact: column orthonormality is U-adjoint times U equals identity, and mutual unbiasedness uses Mathlib complex norm squared. Seven bases are indexed by Fin 7. No quotient by phase, permutation, or global unitary is built into the statement, since these symmetries preserve the predicate and can be used within proofs.

The natural-language source remains authoritative for motivation, while the Lean declaration is authoritative for what Prove2Me will verify. The mission description calls out restrictions where the current formal target is a finite-dimensional core, a fixed interpretation of informal terminology, or one sharpened subquestion from a broader classification problem. Those restrictions should not be silently generalized in a proof claim.

Milestones

Publish an exact three-basis construction in dimension six, then formalize dephasing and obstruction lemmas for extending a partial family.

The capstone is marked as the mission's main item and is never duplicated as a milestone. Definitions precede theorem statements in the proposal order. A milestone is considered complete only when its own exact statement is proved; proving a nearby theorem with stronger-looking prose but mismatched quantifiers, signs, supports, dimensions, or asymptotic constants does not complete it.

Timeline and literature status

The CUHK-Shenzhen AI Math Problems page added this problem on June 23, 2026. At the drafting date, August 31, 2026, the status and target corrections described above were checked against the source page and the cited primary material.

Acceptance criteria

A contribution may prove the displayed theorem or refute it by constructing data satisfying every Lean hypothesis while negating the conclusion. Informal changes of model do not count: any proposed correction must be submitted as a separately reviewed statement with an explanation of which source ambiguity or false implication it repairs. Definitions must remain computational or mathematically constrained; fields that simply assume the desired conclusion are not acceptable. Every proof must compile against the mission's pinned Mathlib revision, use no sorry, and expose a top-level theorem solution when submitted to Prove2Me.

The main theorem is intentionally separated from a smaller milestone. Contributors should preserve that dependency order, publish reusable lemmas rather than monolithic tactics, and report whether a lemma is analytic, algebraic, combinatorial, or infrastructure-only. Numerical evidence, external computer algebra, and exhaustive search are welcome for discovery, but a final certificate must be replayable by Lean. If an external result is invoked, its hypotheses must be represented in the formal statement or proved in the dependency tree.

Formal verification policy

The files were built locally with Lean 4.30.0 and Mathlib revision c5ea00351c28e24afc9f0f84379aa41082b1188f, the supported Prove2Me environment at drafting time. The mission definition file is ordered before all theorem files, and each theorem imports exactly that public definition module or Mathlib. Independent blind read-backs accompany the draft items so reviewers can compare what the Lean code literally says with this mathematical description. Human confirmation remains required before the public proposal can be submitted for moderation.

Selected references

  • Original problem
  • Durt et al., review of MUBs
14 thms5 active usersReviewed
🏆Completed
Machine LearningOperations ResearchStochastic Systems·Captain: tianyipeng

Markov Entanglement: Value Decomposition Error in Multi-agent MDPsResearch Paper

Value decomposition — approximating the value of a joint state by a sum of per-agent local values — is a staple of multi-agent dynamic programming and reinforcement learning, from index policies for restless bandits to modern MARL architectures, yet it is normally used without justification. Chen and Peng (arXiv:2506.02385) supply one. They show a multi-agent MDP admits an exact value decomposition precisely when its transition matrix is not entangled — a notion built in direct analogy with quantum entanglement — and then turn that qualitative characterisation into a quantitative one: a measure of Markov entanglement bounds the decomposition error in general. This mission formalizes that core theory. The goal is Theorem 6, the general N-agent bound in the occupancy-weighted norm; the milestones are the equivalence between separability and exact decomposition, the perturbation machinery that carries a one-step transition error into a value-function error, and the extensions to shared global state and shared rewards. The paper's restless-bandit application, which needs mean-field machinery of its own, is left to a second mission in the series.

25 thms5 active usersReviewed
🏆Completed
Mathematical Physics·Captain: Lucas

Albers–Kiefer–Reginatto 2008: Measurement Analysis and Quantum GravityResearch Paper

Motivation

Whether the gravitational field must be quantized is a long-standing foundational question. A well-known family of arguments tries to settle it by consistency alone: if a classical field were coupled to a quantum system, some basic principle — momentum conservation, the uncertainty relations, or the impossibility of superluminal signalling — would allegedly be violated. The two best-known versions are the gedanken experiment of Eppley and Hannah (1977) and the measurement-theoretic argument of DeWitt (1962).

Albers, Kiefer and Reginatto (Phys. Rev. D 78, 064051 (2008)) re-examine both arguments. Their paper is largely conceptual, but several of its steps are precise, checkable mathematical claims. This mission collects those claims and makes them machine-checkable.

Timeline.

  • 1962 — DeWitt's measurement analysis of the gravitational field, using Peierls brackets and a "universal principle" for apparatus uncertainties.
  • 1977 — Eppley and Hannah's gedanken experiment: a classical gravitational wave scattered off a quantum particle would imply momentum non-conservation, a violation of the uncertainty principle, or superluminal signalling.
  • 2008 — Albers, Kiefer and Reginatto argue that neither gedanken experiment forces quantization, and exhibit a consistent hybrid classical–quantum model.

Setting

Photon pair and detector (Sec. II). Each photon carries a polarization qubit with orthonormal basis ∣↑⟩|\uparrow\rangle∣↑⟩ (horizontal) and ∣↓⟩|\downarrow\rangle∣↓⟩ (vertical). A detector with state space H\mathcal HH (an arbitrary complex inner-product space) is coupled to photon 1. Before the measurement the joint state of photon 1, photon 2 and the detector is

∣Ψ0⟩=12(∣↑⟩1∣↓⟩2−∣↓⟩1∣↑⟩2)∣Φ0⟩,(1)|\Psi_0\rangle = \tfrac{1}{\sqrt2}\bigl(|\uparrow\rangle_1|\downarrow\rangle_2 - |\downarrow\rangle_1|\uparrow\rangle_2\bigr)|\Phi_0\rangle, \qquad (1)∣Ψ0​⟩=2​1​(∣↑⟩1​∣↓⟩2​−∣↓⟩1​∣↑⟩2​)∣Φ0​⟩,(1)

and after the detector has measured photon 1 it is

∣Ψ⟩=12(∣↑⟩1∣↓⟩2∣Φ↑⟩−∣↓⟩1∣↑⟩2∣Φ↓⟩).(2)|\Psi\rangle = \tfrac{1}{\sqrt2}\bigl(|\uparrow\rangle_1|\downarrow\rangle_2|\Phi_\uparrow\rangle - |\downarrow\rangle_1|\uparrow\rangle_2|\Phi_\downarrow\rangle\bigr). \qquad (2)∣Ψ⟩=2​1​(∣↑⟩1​∣↓⟩2​∣Φ↑​⟩−∣↓⟩1​∣↑⟩2​∣Φ↓​⟩).(2)

The reduced density operator of photon 2 is obtained from ∣Ψ⟩⟨Ψ∣|\Psi\rangle\langle\Psi|∣Ψ⟩⟨Ψ∣ by tracing out photon 1 and the detector; the paper asserts that for both states it equals

ρ^=12(∣↑⟩2⟨↑∣2+∣↓⟩2⟨↓∣2).(3)\hat\rho = \tfrac12\bigl(|\uparrow\rangle_2\langle\uparrow|_2 + |\downarrow\rangle_2\langle\downarrow|_2\bigr). \qquad (3)ρ^​=21​(∣↑⟩2​⟨↑∣2​+∣↓⟩2​⟨↓∣2​).(3)

Mass cube and gravitational wave (Sec. III B). The disturbance δT00GW\delta T^{\mathrm{GW}}_{00}δT00GW​ of the averaged energy density of the scattered wave depends on the cube's edge length aaa and speed vvv, with sensitivities Ta=∂ δT00GW/∂aT_a = \partial\,\delta T^{\mathrm{GW}}_{00}/\partial aTa​=∂δT00GW​/∂a and Tv=∂ δT00GW/∂vT_v = \partial\,\delta T^{\mathrm{GW}}_{00}/\partial vTv​=∂δT00GW​/∂v. With Δa≈Δx\Delta a \approx \Delta xΔa≈Δx and Δv≳ℏ/(2mΔx)\Delta v \gtrsim \hbar/(2m\Delta x)Δv≳ℏ/(2mΔx) the uncertainty ΔT00GW=(TaΔa)2+(TvΔv)2\Delta T^{\mathrm{GW}}_{00} = \sqrt{(T_a\Delta a)^2 + (T_v\Delta v)^2}ΔT00GW​=(Ta​Δa)2+(Tv​Δv)2​ has a strictly positive lower bound.

DeWitt's argument (Sec. V). A system observable sss reconstructed from apparatus data has uncertainty Δs2=ΔA2/g2+g2(ΔDΩs)2\Delta s^2 = \Delta A^2/g^2 + g^2(\Delta D_\Omega s)^2Δs2=ΔA2/g2+g2(ΔDΩ​s)2, where ggg is the coupling constant.

Formalization targets

Goal — no signalling through photon 2 (Eq. (3))

For all unit vectors Φ0,Φ↑,Φ↓∈H\Phi_0, \Phi_\uparrow, \Phi_\downarrow \in \mathcal HΦ0​,Φ↑​,Φ↓​∈H,

ρ2(Ψ0)=ρ^andρ2(Ψ)=ρ^.\rho_2(\Psi_0) = \hat\rho \quad\text{and}\quad \rho_2(\Psi) = \hat\rho .ρ2​(Ψ0​)=ρ^​andρ2​(Ψ)=ρ^​.

Milestones

  1. Eq. (1) ⇒ (3): ρ2(Ψ0)=ρ^\rho_2(\Psi_0) = \hat\rhoρ2​(Ψ0​)=ρ^​.
  2. Eq. (2) ⇒ (3): ρ2(Ψ)=ρ^\rho_2(\Psi) = \hat\rhoρ2​(Ψ)=ρ^​.
  3. Sec. III B lower bound: ΔT00GW≥ℏmTvTa\Delta T^{\mathrm{GW}}_{00} \ge \sqrt{\tfrac{\hbar}{m} T_v T_a}ΔT00GW​≥mℏ​Tv​Ta​​ whenever Δx>0\Delta x > 0Δx>0 and Δv≥ℏ/(2mΔx)\Delta v \ge \hbar/(2m\Delta x)Δv≥ℏ/(2mΔx).
  4. Sec. III B optimum: the function Δx↦(TaΔx)2+(Tvℏ/(2mΔx))2\Delta x \mapsto \sqrt{(T_a\Delta x)^2 + (T_v\hbar/(2m\Delta x))^2}Δx↦(Ta​Δx)2+(Tv​ℏ/(2mΔx))2​ attains its minimum over Δx>0\Delta x>0Δx>0 at Δxmin⁡=ℏ2mTvTa\Delta x_{\min} = \sqrt{\tfrac{\hbar}{2m}\tfrac{T_v}{T_a}}Δxmin​=2mℏ​Ta​Tv​​​, with value ℏmTvTa\sqrt{\tfrac{\hbar}{m}T_vT_a}mℏ​Tv​Ta​​.
  5. Eq. (61): min⁡g≠0ΔA2/g2+g2ΔD2=2 ΔA ΔD\min_{g\ne0}\sqrt{\Delta A^2/g^2 + g^2\Delta D^2} = \sqrt{2\,\Delta A\,\Delta D}ming=0​ΔA2/g2+g2ΔD2​=2ΔAΔD​.
  6. Eq. (64): if ΔA ΔC≥ℏ/2\Delta A\,\Delta C \ge \hbar/2ΔAΔC≥ℏ/2 then ℏ ∣Dss∣≤2 ΔA ΔC ∣Dss∣\sqrt{\hbar\,|D_s s|} \le \sqrt{2\,\Delta A\,\Delta C\,|D_s s|}ℏ∣Ds​s∣​≤2ΔAΔC∣Ds​s∣​.

Significance

The goal is the precise content of the paper's rebuttal of the superluminal-signalling branch of the Eppley–Hannah argument: the state of photon 2 accessible to a probe (such as a classical gravitational wave) is the same whether or not photon 1 has been measured, so a probe of photon 2 alone cannot tell whether photon 1 was measured. Milestones 3–6 are the quantitative estimates the paper uses in Sec. III B (a coupling to a test body obeying the uncertainty principle transfers a minimum uncertainty to the classical wave) and Sec. V (DeWitt's limitation on a single observable, Eq. (64)).

The results are elementary once stated; the value of the mission is to pin the paper's claims down exactly, including the hypotheses under which they hold. None of the statements has, to our knowledge, a machine-checked proof on the platform.

Difficulty

The goal and Milestones 1–2 need a workable representation of the partial trace over a factor that is an arbitrary (possibly infinite-dimensional) inner-product space; the cross terms between the two branches vanish because the two branches are orthogonal on photon 1, not because of any property of the detector states. Milestones 3–6 are single-variable optimization statements; the main care is in the side conditions (positivity, excluding g=0g=0g=0 and Δx=0\Delta x = 0Δx=0).

Formalization scope

  • The joint space C2⊗C2⊗H\mathbb C^2\otimes\mathbb C^2\otimes\mathcal HC2⊗C2⊗H is represented by functions {0,1}×{0,1}→H\{0,1\}\times\{0,1\}\to\mathcal H{0,1}×{0,1}→H (component Ψij\Psi_{ij}Ψij​ along ∣i⟩1∣j⟩2|i\rangle_1|j\rangle_2∣i⟩1​∣j⟩2​; index 0=↑0 = \uparrow0=↑, 1=↓1 = \downarrow1=↓). The reduced density matrix of photon 2 has entries ρ2(j,j′)=∑i⟨Ψij′,Ψij⟩H\rho_2(j,j') = \sum_i \langle \Psi_{ij'}, \Psi_{ij}\rangle_{\mathcal H}ρ2​(j,j′)=∑i​⟨Ψij′​,Ψij​⟩H​, and ρ^\hat\rhoρ^​ is a 2×22\times22×2 complex matrix.
  • The detector states are only assumed to be unit vectors. Orthogonality of ∣Φ↑⟩|\Phi_\uparrow\rangle∣Φ↑​⟩ and ∣Φ↓⟩|\Phi_\downarrow\rangle∣Φ↓​⟩ (a physical property of a detector that records the outcome) is not assumed, because Eq. (3) does not depend on it.
  • In Sec. III B the sensitivities Ta,TvT_a, T_vTa​,Tv​, the mass mmm and ℏ\hbarℏ are positive reals; the paper's approximate relations Δa≈Δx\Delta a\approx\Delta xΔa≈Δx and Δp≳ℏ/Δx\Delta p\gtrsim\hbar/\Delta xΔp≳ℏ/Δx are encoded as Δa=Δx\Delta a = \Delta xΔa=Δx and the hypothesis Δv≥ℏ/(2mΔx)\Delta v \ge \hbar/(2m\Delta x)Δv≥ℏ/(2mΔx).
  • In Eq. (61) the coupling constant ranges over all non-zero reals.
  • Sections IV (hybrid classical–quantum ensembles for Nordström gravity) and the appendices are out of scope for this mission.

Selected references

  • M. Albers, C. Kiefer, M. Reginatto, Measurement analysis and quantum gravity, Phys. Rev. D 78, 064051, 2008. https://doi.org/10.1103/PhysRevD.78.064051
  • K. Eppley, E. Hannah, The necessity of quantizing the gravitational field, Found. Phys. 7, 51–68, 1977. https://doi.org/10.1007/BF00715241
  • B. S. DeWitt, Definition of commutators via the uncertainty principle, J. Math. Phys. 3, 619, 1962. https://doi.org/10.1063/1.1724265
8 thms4 active usersReviewed
Theoretical Computer Science·Captain: Goku

Stabilizer Rank of Magic StatesOpen Problem

Motivation

Quantum circuits built from Clifford gates alone are classically simulable in polynomial time. Universality is recovered by adding copies of a magic state, and the fastest known classical simulators of such circuits work by writing the magic-state input as a short linear combination of stabilizer states. The length of the shortest such combination -- the stabilizer rank -- is therefore the exponent governing classical simulation of quantum computation in this model, and lower bounds on it are among the very few unconditional obstructions to classical simulation available at all.

A timeline of what is established for the standard magic state ∣H⟩|H\rangle∣H⟩:

  • 2016. Bravyi, Smith and Smolin exhibit a decomposition giving χ(∣H⊗6⟩)≤7\chi(|H^{\otimes 6}\rangle)\le 7χ(∣H⊗6⟩)≤7, hence χ(∣H⊗n⟩)≤7 n/6≤2 0.468n\chi(|H^{\otimes n}\rangle)\le 7^{\,n/6}\le 2^{\,0.468n}χ(∣H⊗n⟩)≤7n/6≤20.468n, and prove a lower bound of order n\sqrt{n}n​.
  • 2020. Huang, Newman and Szegedy show that hardness assumptions stronger than P≠NP\mathrm{P}\neq\mathrm{NP}P=NP, such as the exponential time hypothesis, imply χ(∣H⊗n⟩)=2Ω(n)\chi(|H^{\otimes n}\rangle)=2^{\Omega(n)}χ(∣H⊗n⟩)=2Ω(n) (arXiv link).
  • 2022. Peleg, Shpilka and Volk improve the unconditional lower bound to Ω(n)\Omega(n)Ω(n) and give the first non-trivial bound for the approximate rank (arXiv:2106.03214).
  • 2024. A quadratic lower bound is obtained for the approximate stabilizer rank (arXiv:2305.10277).

Between the linear unconditional lower bound and the 20.468n2^{0.468n}20.468n upper bound lies the open problem this mission targets.

Setting

Index the computational basis of an nnn-qubit system by bit strings x∈{0,1}nx\in\{0,1\}^nx∈{0,1}n, so a state is a vector ψ∈C2n\psi\in\mathbb{C}^{2^n}ψ∈C2n with coordinates ψ(x)\psi(x)ψ(x).

The Pauli operators are XaZbX^aZ^bXaZb for a,b∈{0,1}na,b\in\{0,1\}^na,b∈{0,1}n, acting by XaZb∣x⟩=(−1) b⋅x∣x⊕a⟩X^aZ^b|x\rangle=(-1)^{\,b\cdot x}|x\oplus a\rangleXaZb∣x⟩=(−1)b⋅x∣x⊕a⟩, where b⋅xb\cdot xb⋅x counts the coordinates on which both are 111 and ⊕\oplus⊕ is bitwise addition; the Pauli group is the set of 4⋅4n4\cdot 4^n4⋅4n operators icXaZbi^cX^aZ^bicXaZb. A unitary UUU is a Clifford unitary when UPU†UPU^\daggerUPU† lies in the Pauli group for every Pauli group element PPP, and a stabilizer state is a vector U∣0⋯0⟩U|0\cdots0\rangleU∣0⋯0⟩ for some Clifford UUU. There are 2n∏k=1n(2k+1)2^n\prod_{k=1}^{n}(2^k+1)2n∏k=1n​(2k+1) of them up to phase -- six for a single qubit.

The stabilizer rank χ(ψ)\chi(\psi)χ(ψ) is the least rrr admitting coefficients c1,…,cr∈Cc_1,\dots,c_r\in\mathbb{C}c1​,…,cr​∈C and stabilizer states φ1,…,φr\varphi_1,\dots,\varphi_rφ1​,…,φr​ with ψ=∑j≤rcjφj\psi=\sum_{j\le r}c_j\varphi_jψ=∑j≤r​cj​φj​.

The magic state is ∣H⟩=cos⁡(π/8)∣0⟩+sin⁡(π/8)∣1⟩|H\rangle=\cos(\pi/8)|0\rangle+\sin(\pi/8)|1\rangle∣H⟩=cos(π/8)∣0⟩+sin(π/8)∣1⟩, and ∣H⊗n⟩|H^{\otimes n}\rangle∣H⊗n⟩ its nnn-fold tensor power, with coordinates cos⁡(π/8) n−∣x∣sin⁡(π/8) ∣x∣\cos(\pi/8)^{\,n-|x|}\sin(\pi/8)^{\,|x|}cos(π/8)n−∣x∣sin(π/8)∣x∣ where ∣x∣|x|∣x∣ is the Hamming weight of xxx.

Target

The goal is a super-polynomial lower bound: for every exponent ddd and constant CCC there exists nnn with

χ(∣H⊗n⟩)  >  C nd,\chi\bigl(|H^{\otimes n}\rangle\bigr)\;>\;C\,n^{d},χ(∣H⊗n⟩)>Cnd,

equivalently, χ(∣H⊗n⟩)\chi(|H^{\otimes n}\rangle)χ(∣H⊗n⟩) is not O(nd)O(n^d)O(nd) for any fixed ddd.

Stronger statements are expected but are deliberately not the goal. An exponential bound χ=2Ω(n)\chi=2^{\Omega(n)}χ=2Ω(n) is believed and follows from hardness assumptions, but a goal naming a specific growth rate would be superseded by the next improvement; super-polynomiality is the weakest statement that settles the question of principle.

Significance

The result itself. A super-polynomial lower bound would unconditionally rule out efficient classical simulation of Clifford-plus-magic-state circuits by stabilizer decomposition, currently the leading such technique. The converse direction shows how much is at stake: a polynomial upper bound on χ(∣H⊗n⟩)\chi(|H^{\otimes n}\rangle)χ(∣H⊗n⟩) would imply BPP=BQP\mathrm{BPP}=\mathrm{BQP}BPP=BQP, and via postselection P=NP\mathrm{P}=\mathrm{NP}P=NP. There is also a purely classical payoff -- improving the known bound even to super-linear would produce a Boolean function computable in polynomial time requiring a super-linear number of summands in any decomposition into exponentials of quadratic forms over F2\mathbb{F}_2F2​, resolving a separate open question.

Formalizing it. The goal is open, so no known proof is being transcribed. What the mission produces is a machine-checked statement of the problem together with formalizations of the established bounds, none of which has a machine-checked proof anywhere. It also produces the first Pauli/Clifford/stabilizer layer in Lean: no existing Lean library contains the nnn-qubit Pauli group, the Clifford group, or stabilizer states, and that layer is reusable for stabilizer error correction, magic monotones, and Clifford simulation generally.

Difficulty

Counting settles the problem for random states: the stabilizer states are too few for short combinations to cover a generic state, so almost every state has exponential stabilizer rank. This says nothing about ∣H⊗n⟩|H^{\otimes n}\rangle∣H⊗n⟩, which is a single explicit, highly structured vector, and the entire difficulty is that lower bounds must be proved for that specific state rather than for a typical one. Every newcomer proposes the counting argument; it does not apply.

The known techniques reduce the question to statements about decompositions of explicit Boolean functions into quadratic-form exponentials, and the barrier is quantitative: the available arguments lose a factor that caps them at linear bounds. The source of the current record documents explicitly why its method cannot pass super-linear, and the fact that going beyond linear would resolve an independent open problem in Boolean function complexity indicates the obstruction is not merely technical.

Formalization scope

State vectors are functions {0,1}n→C\{0,1\}^n\to\mathbb{C}{0,1}n→C and are not required to be normalised; normalisation does not affect the rank, and stabilizer states are unit vectors automatically as Clifford images of ∣0⋯0⟩|0\cdots0\rangle∣0⋯0⟩. The Pauli group is given by the explicit parametrisation icXaZbi^cX^aZ^bicXaZb rather than an abstract presentation, and the Clifford group is characterised as its unitary normaliser, equivalent to the usual generated-by-H,S,CNOTH,S,\mathrm{CNOT}H,S,CNOT description. Because eiθUe^{i\theta}UeiθU normalises the Pauli group whenever UUU does, the stabilizer states are closed under global phase; this is harmless, as the coefficients are arbitrary complex numbers.

One trivialising reading must be excluded. The rank is defined as an infimum over a set of natural numbers, and Lean gives the empty infimum the value 000; if no decomposition existed the rank would be 000 for every state and the goal would be false rather than merely unproved. The milestone χ(ψ)≤2n\chi(\psi)\le 2^nχ(ψ)≤2n is what certifies the set is nonempty, making the rank a genuine minimum, and it should be proved first. Separately, the goal quantifies CCC over all reals including negative values, for which the inequality is trivially satisfiable; the content lies in large positive CCC.

A complete development needs, beyond the published definitions, the correspondence between stabilizer states and affine subspaces carrying quadratic phase functions, on which all known lower-bound arguments rest. Contributions of any milestone are welcome, as are function-level reformulations of the rank and the equivalent characterisation of stabilizer states via maximal abelian Pauli subgroups.

Selected references

  • S. Peleg, A. Shpilka, B. L. Volk, Lower Bounds on Stabilizer Rank, Quantum 6 (2022) 652; arXiv:2106.03214.
  • S. Bravyi, G. Smith, J. Smolin, Trading Classical and Quantum Computational Resources, Phys. Rev. X 6 (2016) 021043; arXiv:1506.01396.
  • C. Huang, M. Newman, M. Szegedy, Explicit Lower Bounds on Strong Quantum Simulation, IEEE Trans. Inf. Theory 66(9) (2020) 5585--5600.
  • Quadratic Lower Bounds on the Approximate Stabilizer Rank: A Probabilistic Approach, STOC 2024; arXiv:2305.10277.
5 thms4 active usersReviewed
Information TheoryTheoretical Computer Science·Captain: mikedeng1

Shadow Tomography of Quantum States 3: Shadow Tomography Needs Ω(min{D², log M}/ε²) CopiesResearch Paper

Why count copies of a quantum state

A mixed state of a DDD-dimensional quantum system is a D×DD\times DD×D positive semidefinite matrix ρ\rhoρ of trace 111. Writing ρ\rhoρ down takes about D2D^2D2 real numbers, and DDD is exponential in the number of qubits, so learning ρ\rhoρ in full is expensive. Holevo's theorem and the random access code bounds of Ambainis, Nayak, Ta-Shma and Vazirani say that an nnn-qubit state carries far fewer usable classical bits than its 2n2^n2n amplitudes suggest. Shadow tomography, introduced by S. Aaronson (arXiv:1711.01053), makes this quantitative: given MMM known two-outcome measurements E1,…,EME_1,\dots,E_ME1​,…,EM​, how many copies of an unknown ρ\rhoρ are needed to estimate every acceptance probability Tr⁡(Eiρ)\operatorname{Tr}(E_i\rho)Tr(Ei​ρ) to within ε\varepsilonε?

Aaronson shows that O~(log⁡4M⋅log⁡D/ε4)\widetilde O(\log^4 M\cdot\log D/\varepsilon^4)O(log4M⋅logD/ε4) copies suffice, polylogarithmic in MMM and DDD. The question this mission addresses is the converse: how many copies are necessary. Section 6 of the paper proves two lower bounds. Theorem 16 gives Ω(min⁡{D,log⁡M}/ε2)\Omega(\min\{D,\log M\}/\varepsilon^2)Ω(min{D,logM}/ε2) even when ρ\rhoρ and the EiE_iEi​ are diagonal (a classical distribution). Theorem 19, the goal here, strengthens the dimension term to D2D^2D2 for genuinely quantum states.

Timeline:

  • 2016: full tomography of a DDD-dimensional state to trace-distance accuracy ε\varepsilonε needs Θ(D2/ε2)\Theta(D^2/\varepsilon^2)Θ(D2/ε2) copies up to logarithmic factors, with upper and lower bounds by O'Donnell and Wright (arXiv:1508.01907) and Haah, Harrow, Ji, Wu and Yu (arXiv:1508.01797).
  • 2017–2018: Aaronson poses shadow tomography (Problem 1), proves the polylogarithmic upper bound (Theorem 2), and proves the lower bounds of Theorems 16 and 19.
  • 2020 onward: classical shadows (Huang, Kueng and Preskill, arXiv:2002.08953) and later shadow-tomography algorithms study the same estimation task with other measurement models and improved upper bounds.

Setting

Fix a dimension DDD. A two-outcome measurement is a D×DD\times DD×D Hermitian matrix EEE with 0⪯E⪯I0\preceq E\preceq I0⪯E⪯I; it accepts ρ\rhoρ with probability Tr⁡(Eρ)\operatorname{Tr}(E\rho)Tr(Eρ). The tensor power ρ⊗k\rho^{\otimes k}ρ⊗k is the Dk×DkD^k\times D^kDk×Dk matrix of kkk independent copies. A strategy using kkk copies is a measurement of ρ⊗k\rho^{\otimes k}ρ⊗k with finitely many outcomes ω∈Ω\omega\in\Omegaω∈Ω, given by positive semidefinite matrices Πω\Pi_\omegaΠω​ with ∑ωΠω=I\sum_\omega\Pi_\omega = I∑ω​Πω​=I, together with outputs b(ω)=(b1(ω),…,bM(ω))b(\omega)=(b_1(\omega),\dots,b_M(\omega))b(ω)=(b1​(ω),…,bM​(ω)). It succeeds on ρ\rhoρ when, with probability at least 2/32/32/3 over ω\omegaω, ∣bi(ω)−Tr⁡(Eiρ)∣≤ε|b_i(\omega)-\operatorname{Tr}(E_i\rho)|\le\varepsilon∣bi​(ω)−Tr(Ei​ρ)∣≤ε for every i∈[M]i\in[M]i∈[M].

The proof works with these further objects, all defined in the mission:

  • an orthogonal projection P\mathbb PP onto an N/2N/2N/2-dimensional subspace of CN\mathbb C^NCN (Hermitian, idempotent, trace N/2N/2N/2);
  • ρP:=2NP\rho_{\mathbb P} := \tfrac2N\mathbb PρP​:=N2​P, the maximally mixed state on that subspace;
  • σP,ε:=(1−6ε) I/N+6ε ρP\sigma_{\mathbb P,\varepsilon} := (1-6\varepsilon)\,\mathbb I/N + 6\varepsilon\,\rho_{\mathbb P}σP,ε​:=(1−6ε)I/N+6ερP​;
  • the von Neumann entropy in bits, S(ρ)=−∑xλxlog⁡2λxS(\rho) = -\sum_x\lambda_x\log_2\lambda_xS(ρ)=−∑x​λx​log2​λx​ over the eigenvalues of ρ\rhoρ;
  • for states σ1,…,σK\sigma_1,\dots,\sigma_Kσ1​,…,σK​ and ζ:=1K∑iσi⊗T\zeta := \tfrac1K\sum_i\sigma_i^{\otimes T}ζ:=K1​∑i​σi⊗T​, the quantum mutual information with the classical index, I(ζ;i):=S(ζ)−1K∑iS(σi⊗T)I(\zeta;i) := S(\zeta) - \tfrac1K\sum_i S(\sigma_i^{\otimes T})I(ζ;i):=S(ζ)−K1​∑i​S(σi⊗T​).

Formalization targets

Goal: Theorem 19 (p. 23)

There are a universal constant c>0c>0c>0 and a threshold N0N_0N0​ such that for all D≥N0D\ge N_0D≥N0​, all MMM with log⁡2M≥N02\log_2 M\ge N_0^2log2​M≥N02​ and all 0<ε≤160<\varepsilon\le\tfrac160<ε≤61​, some measurements E1,…,EME_1,\dots,E_ME1​,…,EM​ on CD\mathbb C^DCD force every strategy that succeeds on every mixed state to use

k  ≥  c min⁡{D2, log⁡2M}ε2k \;\ge\; c\,\frac{\min\{D^2,\ \log_2 M\}}{\varepsilon^2}k≥cε2min{D2, log2​M}​

copies. The goal fixes only the shape Ω(min⁡{D2,log⁡M}/ε2)\Omega(\min\{D^2,\log M\}/\varepsilon^2)Ω(min{D2,logM}/ε2), not a constant, so a sharper constant does not invalidate it.

Milestones (pp. 23–24)

The milestones follow the proof, which sets N:=⌊min⁡{D,log⁡2M}⌋N:=\lfloor\min\{D,\sqrt{\log_2 M}\}\rfloorN:=⌊min{D,log2​M​}⌋ and K:=⌊cN2⌋K:=\lfloor c^{N^2}\rfloorK:=⌊cN2⌋:

  1. Eq. (2): for some c∈(1,2)c\in(1,2)c∈(1,2) and all large even NNN there are KKK projections Pi\mathbb P_iPi​ of rank N/2N/2N/2 with ∣Tr⁡(Piρj)−12∣≤112|\operatorname{Tr}(\mathbb P_i\rho_j)-\tfrac12|\le\tfrac1{12}∣Tr(Pi​ρj​)−21​∣≤121​ for all i≠ji\ne ji=j.
  2. Tr⁡(Piσi)=12+3ε\operatorname{Tr}(\mathbb P_i\sigma_i) = \tfrac12+3\varepsilonTr(Pi​σi​)=21​+3ε.
  3. ∣Tr⁡(Pjσi)−12∣=6ε∣Tr⁡(Pjρi)−12∣≤ε2|\operatorname{Tr}(\mathbb P_j\sigma_i)-\tfrac12| = 6\varepsilon|\operatorname{Tr}(\mathbb P_j\rho_i)-\tfrac12|\le\tfrac\varepsilon2∣Tr(Pj​σi​)−21​∣=6ε∣Tr(Pj​ρi​)−21​∣≤2ε​ for i≠ji\neq ji=j.
  4. The exact entropy S(σi)=log⁡2N−[1−h(12+3ε)]S(\sigma_i) = \log_2 N - [1-h(\tfrac12+3\varepsilon)]S(σi​)=log2​N−[1−h(21​+3ε)], with hhh the binary entropy, and the bound S(σi)≥log⁡2N−Cε2S(\sigma_i)\ge\log_2 N - C\varepsilon^2S(σi​)≥log2​N−Cε2.
  5. I(ζ;i)≤T(log⁡2N−S(σi))I(\zeta;i)\le T(\log_2 N - S(\sigma_i))I(ζ;i)≤T(log2​N−S(σi​)) when all σi\sigma_iσi​ have equal entropy.

Significance

Theorem 19 shows that the log⁡M\log MlogM dependence of shadow tomography cannot be removed, and that for M≥2D2M\ge 2^{D^2}M≥2D2 shadow tomography is as hard as full tomography: as MMM grows the bound becomes the Ω(D2/ε2)\Omega(D^2/\varepsilon^2)Ω(D2/ε2) tomography lower bound, which it therefore contains. Compared with the classical Theorem 16, it shows that quantum states need quadratically more copies in the dimension term. The 1/ε21/\varepsilon^21/ε2 factor matches the upper bound of Proposition 20 for the decision version, so the ε\varepsilonε-dependence of the lower bound is tight in that setting.

The results are proved in the paper; none of them has a machine-checked proof that we know of. Formalizing the argument requires von Neumann entropy and its additivity on tensor products, the bound S≤log⁡2(dimension)S\le\log_2(\text{dimension})S≤log2​(dimension), a Holevo-plus-Fano step that turns successful estimation into mutual information, and the existence of many nearly orthogonal half-dimensional subspaces. Each is reusable well beyond this mission.

Difficulty

The obvious attempt adapts the classical argument of Theorem 16, which hides KKK subsets of [N][N][N] in a biased distribution. Quantum states allow exp⁡(Ω(N2))\exp(\Omega(N^2))exp(Ω(N2)) hidden subspaces instead of exp⁡(Ω(N))\exp(\Omega(N))exp(Ω(N)) subsets, which is where D2D^2D2 comes from, but two steps change character. First, the hiding family must be shown to exist: Eq. (2) is a concentration statement for random subspaces, and the lemma the paper cites for it (Lemma 18) is misstated, as explained below. Second, the information bound must be carried out for quantum states: the step "learning iii from ζ\zetaζ requires I(ζ;i)≥log⁡2KI(\zeta;i)\ge\log_2 KI(ζ;i)≥log2​K" needs Holevo's bound and Fano's inequality, adjusted for success probability 2/32/32/3 rather than certainty.

Formalization scope

Matrices are Matrix n n ℂ over a finite index type; the goal uses n=Fin Dn=\texttt{Fin } Dn=Fin D. Conventions committed to:

  • A mixed state is the published WildeQIT.IsDensityOperator (positive semidefinite, trace 111), reused as a reference item.
  • A strategy is a finite-outcome POVM on ρ⊗k\rho^{\otimes k}ρ⊗k, indexed by Fin k → Fin D, with deterministic outputs b(ω)∈RMb(\omega)\in\mathbb R^Mb(ω)∈RM; classical randomness can be absorbed into the outcome set.
  • Probabilities and traces are real parts of complex traces. Entropies use log⁡2\log_2log2​; Lean's log⁡20=0\log_2 0 = 0log2​0=0 gives 0log⁡0=00\log 0=00log0=0, and vnEntropy returns 000 on non-Hermitian matrices, which no statement uses.
  • Added to the goal: the threshold N0N_0N0​ on DDD and log⁡2M\sqrt{\log_2 M}log2​M​ (for D=1D=1D=1, k=0k=0k=0 succeeds) and ε≤16\varepsilon\le\tfrac16ε≤61​ (for ε≥12\varepsilon\ge\tfrac12ε≥21​, the output bi=12b_i=\tfrac12bi​=21​ succeeds with k=0k=0k=0). Problem 1's bi∈[0,1]b_i\in[0,1]bi​∈[0,1] is dropped, an equivalent statement under clipping.
  • The measurements are chosen before the strategy (for every strategy, the same EEE), which is the meaning of a lower bound.

A trivializing formalization is ruled out: the goal does not mention NNN, KKK, Pi\mathbb P_iPi​, σi\sigma_iσi​ or ζ\zetaζ, the success condition quantifies over all mixed states rather than a vacuous class, and the measurements are fixed before the strategy.

Not drafted:

  • Lemma 18 (pp. 22–23) is false as printed. Since ES[ρS]=I/N\mathbb E_S[\rho_S] = \mathbb I/NES​[ρS​]=I/N, Tr⁡(PTρS)\operatorname{Tr}(\mathbb P_T\rho_S)Tr(PT​ρS​) concentrates at 1/21/21/2, not 1/41/41/4. The proof needs only Eq. (2), which is centred correctly and is a milestone.
  • Eq. (2) is stated as existence, not as "probability 1−o(1)1-o(1)1−o(1) over Haar-random subspaces", because Mathlib has no Haar measure on the unitary group or the Grassmannian; existence is what the proof uses.
  • "I(ζ;i)I(\zeta;i)I(ζ;i) must be at least log⁡2K\log_2 Klog2​K" is not drafted: as stated it is imprecise for success probability 2/32/32/3, and the correct Holevo–Fano form is left to the solver.
  • The final combination I(ζ;i)=O(Tε2)I(\zeta;i)=O(T\varepsilon^2)I(ζ;i)=O(Tε2), T=Ω(N2/ε2)T=\Omega(N^2/\varepsilon^2)T=Ω(N2/ε2) is the goal's last step.

Contributions welcome: proofs of the milestones; a Haar-measure version of Eq. (2); von Neumann entropy infrastructure (additivity, the dimension bound, concavity); Holevo's bound and Fano's inequality for finite-dimensional states.

Selected references

  • S. Aaronson, Shadow Tomography of Quantum States, arXiv:1711.01053v2, 2018; STOC 2018. https://arxiv.org/abs/1711.01053
  • P. Hayden, D. Leung, A. Winter, Aspects of generic entanglement, Comm. Math. Phys. 265, 2006. https://arxiv.org/abs/quant-ph/0407049
  • R. O'Donnell, J. Wright, Efficient quantum tomography, STOC 2016. https://arxiv.org/abs/1508.01907
  • J. Haah, A. W. Harrow, Z. Ji, X. Wu, N. Yu, Sample-optimal tomography of quantum states, IEEE Trans. Inf. Theory 63, 2017. https://arxiv.org/abs/1508.01797
  • A. Ambainis, A. Nayak, A. Ta-Shma, U. Vazirani, Dense quantum coding and quantum finite automata, J. ACM 49, 2002. https://arxiv.org/abs/quant-ph/9804043
  • H.-Y. Huang, R. Kueng, J. Preskill, Predicting many properties of a quantum system from very few measurements, Nature Physics 16, 2020. https://arxiv.org/abs/2002.08953
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Information TheoryStatistics·Captain: mikedeng1

Shadow Tomography of Quantum States 2: Even the Classical Special Case Needs Ω(min{D, log M}/ε²) CopiesResearch Paper

Why the copy count matters

Shadow tomography asks for predictions of many specified measurements of an unknown quantum state, while using as few prepared copies of that state as possible. The requested output is a list of acceptance probabilities, not a full description of the state. A procedure might exploit the fact that these are only MMM numbers, even when the state has dimension DDD. The natural question is how far this saving can go. Aaronson's paper gives upper bounds and separates two sources of difficulty: one already present for ordinary probability distributions, and one arising from noncommuting quantum measurements.

This mission concerns the first source. It formalizes Theorem 16, which says that even when the state and every requested measurement are diagonal in the same basis, the number of copies must grow with min⁡{D,log⁡M}/ε2\min\{D,\log M\}/\varepsilon^2min{D,logM}/ε2 in the relevant asymptotic regime. In that special case, the unknown state is an ordinary distribution on DDD outcomes. The theorem therefore puts a limit on any proposed improvement to shadow tomography that would promise fewer copies in all instances.

States, measurements, and estimates

A mixed state ρ\rhoρ on a DDD-dimensional system is a positive semidefinite D×DD\times DD×D complex matrix with trace one. A two-outcome measurement is represented by an effect EEE satisfying 0⪯E⪯I0\preceq E\preceq I0⪯E⪯I; it accepts ρ\rhoρ with probability Tr⁡(Eρ)\operatorname{Tr}(E\rho)Tr(Eρ). When ρ\rhoρ is diagonal, its diagonal entries are the probabilities of the DDD basis outcomes. When EEE is diagonal too, it specifies a randomized yes-or-no test on those outcomes. These conventions are stated in Section 3 of the paper.

Given effects E1,…,EME_1,\ldots,E_ME1​,…,EM​, a shadow-tomography strategy measures kkk independent copies ρ⊗k\rho^{\otimes k}ρ⊗k and outputs estimates b1,…,bMb_1,\ldots,b_Mb1​,…,bM​. It succeeds on ρ\rhoρ when every estimate differs from Tr⁡(Eiρ)\operatorname{Tr}(E_i\rho)Tr(Ei​ρ) by at most ε\varepsilonε. The lower bound requires success probability at least 2/32/32/3 for every diagonal mixed state. The strategy may make a joint quantum measurement on all copies and may choose its estimates from its observed outcome. This is the same measurement model used in Problem 1.

Formalization targets

Classical special-case lower bound

The goal is the classical clause of Theorem 16. There are absolute constants c>0c>0c>0 and N0N_0N0​ such that, for D≥N0D\ge N_0D≥N0​, log⁡2M≥N0\log_2 M\ge N_0log2​M≥N0​, and 0<ε≤1/60<\varepsilon\le1/60<ε≤1/6, there are MMM diagonal effects with 0/1 entries, corresponding to the known Boolean functions in Section 6.1, for which every strategy successful on all diagonal states must use

k≥c min⁡{D,log⁡2M}ε2.k\ge c\,\frac{\min\{D,\log_2 M\}}{\varepsilon^2}.k≥cε2min{D,log2​M}​.

The hard measurements are chosen before the strategy is quantified. The statement therefore also rules out a strategy with a smaller copy count that works uniformly for all quantum states and measurements. Its constants and threshold express the Ω\OmegaΩ notation in Theorem 16, rather than specifying a numerical optimum.

Supporting targets

Four milestones come from the proof on pages 20–21: the high-probability overlap bound for independently chosen half-size subsets (Eq. (1)); the acceptance probability of a subset under its associated biased distribution; an upper bound on the mutual information between the hidden subset index and the observed samples; and the exact entropy formula with a quadratic entropy deficit. These statements expose the combinatorial and information-theoretic parts of the lower bound while leaving the goal as the paper's copy-complexity result.

What the result supplies

Theorem 16 sets a floor for shadow tomography that survives even when all operators commute. Any uniform copy bound for the full quantum task must respect this floor. The result also distinguishes the difficulty of predicting many properties of a distribution from the extra difficulty possible for noncommuting states and measurements, which the paper treats in a separate lower bound. Section 6 presents both bounds.

A complete formalization would give machine-checked statements and proofs for the finite subset construction, the entropy calculation, the information inequality, and the reduction from a successful quantum measurement procedure on diagonal states to a lower bound on kkk. The theorem is proved on paper; these draft statements are open Lean goals and do not claim that its proof has been machine checked. The finite-distribution and information-theory infrastructure is reusable for other lower bounds based on hidden-index families.

Where the argument is delicate

Counting how many possible measurements there are does not by itself show that samples reveal enough about which distribution generated them. The lower bound needs a quantitative relation between estimation accuracy and information about a hidden index, while each individual sample carries limited information. The paper's printed overlap condition (1) is too weak for the next displayed ε/2\varepsilon/2ε/2 estimate: at its boundary it gives ε\varepsilonε. The milestone preserves Eq. (1) as printed; closing the goal requires the correspondingly sharper overlap fact with N/24N/24N/24, which follows from the same type of concentration statement after adjusting its constant. The printed assertion that learning the index requires mutual information at least log⁡2K\log_2 Klog2​K is also imprecise at success probability 2/32/32/3; a quantitative decoding inequality is needed. Neither incorrect display is a draft milestone.

Formalization scope

Matrices are indexed by Fin D. WildeQIT.IsDensityOperator supplies the mixed-state predicate ρ⪰0\rho\succeq0ρ⪰0 and Tr⁡(ρ)=1\operatorname{Tr}(\rho)=1Tr(ρ)=1. Diagonal states and effects use the standard matrix diagonal predicate. An effect is positive semidefinite together with its complement. The tensor power uses functions Fin k → Fin D as basis indices; at k=0k=0k=0 it is a one-by-one identity matrix. A strategy is a finite-outcome POVM on that tensor power, followed by a real estimate vector for each outcome. The output values are not restricted to [0,1][0,1][0,1]: clipping them to this interval cannot worsen an estimate of a probability. No restriction to classical estimators is placed in the goal; that would change the allowed strategies before the theorem has been proved.

The asymptotic threshold excludes the one-dimensional and single-measurement corners where the claimed rate does not describe the problem. The bound ε≤1/6\varepsilon\le1/6ε≤1/6 keeps the biased distributions used on page 20 nonnegative; ε≥1/2\varepsilon\ge1/2ε≥1/2 would permit a zero-copy constant estimate. Subset milestones require even NNN or explicitly require a half-size subset, so N/2N/2N/2 has its intended meaning. The natural logarithm appears nowhere in the lower-bound rate; entropy, mutual information, and log⁡2M\log_2 Mlog2​M use base two. At zero probability the entropy convention is 0log⁡0=00\log 0=00log0=0.

The finite distributions, entropy, conditional entropy, and mutual information reuse the published WildeQIT definitions. Contributions that prove the four source milestones, establish the sharper overlap fact, or supply the quantitative decoding step are welcome. The goal must retain its order of quantifiers: one hard measurement family, then every strategy, with success demanded on every diagonal state.

Selected references

  • Scott Aaronson, Shadow Tomography of Quantum States, arXiv preprint arXiv:1711.01053v2, 2018. Preprint.
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Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer 3: The Success Probability of Quantum Order FindingResearch Paper

Motivation

The security of the RSA cryptosystem rests on the assumed difficulty of factoring large integers, and the best known classical algorithms for factoring run in super-polynomial time. In 1994 Peter Shor showed that a quantum computer can factor an nnn-digit integer in time polynomial in nnn (Shor, SIAM J. Comput. 1997; conference version FOCS 1994). The algorithm has two parts. A classical reduction, due to Miller (1976), turns factoring into order finding: given xxx coprime to nnn, find the least r≥1r \ge 1r≥1 with xr≡1(modn)x^r \equiv 1 \pmod nxr≡1(modn). The quantum part solves order finding.

This mission formalizes the quantum part as Shor analyzes it in §5 of the journal paper: the construction of the quantum state, the probability of each measurement outcome, and the classical post-processing that reads rrr off the measured value. The paper's claim is that one run of this procedure returns rrr with probability at least φ(r)/3r\varphi(r)/3rφ(r)/3r.

Timeline:

  • 1976: Miller reduces factoring to order finding (with randomization).
  • 1985–1994: Deutsch, Bernstein–Vazirani and Simon give the quantum Fourier sampling ideas the algorithm builds on.
  • 1994: Shor's FOCS paper introduces the factoring and discrete logarithm algorithms.
  • 1997: the SIAM J. Comput. version gives the analysis formalized here, with qqq the power of 222 in [n2,2n2)[n^2, 2n^2)[n2,2n2).

Setting

Fix an integer n≥2n \ge 2n≥2 and an integer xxx coprime to nnn. Its order rrr is the least r≥1r \ge 1r≥1 with xr≡1(modn)x^r \equiv 1 \pmod nxr≡1(modn); since xxx is a unit, r≤φ(n)<nr \le \varphi(n) < nr≤φ(n)<n. Let q=2lq = 2^lq=2l be the power of 222 with n2≤q<2n2n^2 \le q < 2n^2n2≤q<2n2.

A quantum state on two registers, the first holding 0≤a<q0 \le a < q0≤a<q and the second a residue y∈Z/ny \in \mathbb{Z}/ny∈Z/n, is a complex vector ψ(a,y)\psi(a, y)ψ(a,y) indexed by the basis states ∣a,y⟩|a, y\rangle∣a,y⟩. Measuring it returns ∣a,y⟩|a, y\rangle∣a,y⟩ with probability ∣ψ(a,y)∣2|\psi(a, y)|^2∣ψ(a,y)∣2.

The Fourier matrix AqA_qAq​ is the q×qq \times qq×q matrix with entries (Aq)a,c=q−1/2exp⁡(2πiac/q)(A_q)_{a,c} = q^{-1/2}\exp(2\pi i a c/q)(Aq​)a,c​=q−1/2exp(2πiac/q), with rows indexing inputs and columns outputs. The algorithm

  1. prepares 1q1/2∑a=0q−1∣a⟩∣xa mod n⟩\frac{1}{q^{1/2}}\sum_{a=0}^{q-1}|a\rangle|x^a \bmod n\rangleq1/21​∑a=0q−1​∣a⟩∣xamodn⟩ (eq. (5.2)),
  2. applies AqA_qAq​ to the first register, obtaining 1q∑a,cexp⁡(2πiac/q)∣c⟩∣xa mod n⟩\frac1q\sum_{a,c}\exp(2\pi iac/q)|c\rangle|x^a \bmod n\rangleq1​∑a,c​exp(2πiac/q)∣c⟩∣xamodn⟩ (eq. (5.4)),
  3. measures, obtaining some ∣c,y⟩|c, y\rangle∣c,y⟩,
  4. rounds c/qc/qc/q to the nearest fraction with denominator smaller than nnn.

The observed ccc gives us rrr if some fraction with lowest-terms denominator below nnn is within 1/2q1/2q1/2q of c/qc/qc/q, and every such fraction has lowest-terms denominator exactly rrr. In the Lean development these objects are preFourierState, finalState, outcomeProb and yieldsOrder, in the namespace ShorAlgorithms.OrderFinding, and the shared definition ShorAlgorithms.Shared.fourierMatrix.

Formalization targets

Goal: success probability at least φ(r)/3r\varphi(r)/3rφ(r)/3r

For all sufficiently large nnn, with xxx, rrr and qqq as above,

Pr⁡[the observed c gives us r]  =  ∑c gives r ∑y∈Z/n∣Ψ(c,y)∣2  ≥  φ(r)3r,\Pr\bigl[\text{the observed } c \text{ gives us } r\bigr] \;=\; \sum_{c\ \text{gives}\ r}\ \sum_{y \in \mathbb{Z}/n} |\Psi(c, y)|^2 \;\ge\; \frac{\varphi(r)}{3r},Pr[the observed c gives us r]=c gives r∑​ y∈Z/n∑​∣Ψ(c,y)∣2≥3rφ(r)​,

where Ψ\PsiΨ is the state (5.4). The threshold on nnn is uniform in xxx and qqq; it is the paper's "for sufficiently large nnn" from the per-state bound.

Milestones

  1. Eqs. (5.5)–(5.6). For 0≤k<r0 \le k < r0≤k<r, the probability of ∣c,xk⟩|c, x^k\rangle∣c,xk⟩ equals ∣1q∑b=0⌊(q−k−1)/r⌋exp⁡(2πi(br+k)c/q)∣2\left|\frac1q\sum_{b=0}^{\lfloor (q-k-1)/r\rfloor}\exp(2\pi i(br+k)c/q)\right|^2​q1​∑b=0⌊(q−k−1)/r⌋​exp(2πi(br+k)c/q)​2.
  2. Eq. (5.11). For nnn past a threshold, every ∣c,xk⟩|c, x^k\rangle∣c,xk⟩ with −r/2≤rc−dq≤r/2-r/2 \le rc - dq \le r/2−r/2≤rc−dq≤r/2 for some integer ddd has probability at least 1/3r21/3r^21/3r2.
  3. Eq. (5.13). If n2≤qn^2 \le qn2≤q, at most one fraction with denominator below nnn lies within 1/2q1/2q1/2q of c/qc/qc/q.
  4. p. 1500. Such a fraction is a convergent of the continued fraction of c/qc/qc/q.
  5. p. 1501. At least φ(r)\varphi(r)φ(r) values of ccc are within 1/2q1/2q1/2q of some d/rd/rd/r with gcd⁡(d,r)=1\gcd(d, r) = 1gcd(d,r)=1; with the rrr distinct values of xkx^kxk this gives at least rφ(r)r\varphi(r)rφ(r) states ∣c,xk⟩|c, x^k\rangle∣c,xk⟩, and each such ccc gives us rrr.

Significance

The goal is the quantitative statement behind "order finding is in bounded-error quantum polynomial time": since φ(r)/r≥δ/log⁡log⁡r\varphi(r)/r \ge \delta/\log\log rφ(r)/r≥δ/loglogr for a constant δ\deltaδ (Hardy and Wright, Thm. 328), O(log⁡log⁡r)O(\log\log r)O(loglogr) repetitions find rrr with high probability, and Miller's reduction then factors nnn. Without the bound, the algorithm is a procedure with no guarantee.

The result is proved, in the paper and in textbooks (Nielsen and Chuang, 2000, §5.3), usually with a phase-estimation analysis rather than Shor's direct count. What this mission adds is a machine-checked proof of Shor's own argument, with his choice of qqq and his constants, starting from the state built by applying AqA_qAq​ to (5.2). Formal proofs of idealized versions exist elsewhere, for instance in the exact-period model where rrr divides qqq and the output is uniform on rrr peaks, but that model removes the approximation that the 1/3r21/3r^21/3r2 bound is about. Legendre's theorem on continued fractions is already on the platform (FamousTheorems.legendre_continued_fraction_theorem) and is included as a reference item.

Difficulty

The obvious route is to compute the output distribution in closed form. That works only when rrr divides qqq; here qqq is a power of 222 and rrr is arbitrary, so the amplitudes are geometric sums of ⌊(q−k−1)/r⌋+1\lfloor (q-k-1)/r\rfloor + 1⌊(q−k−1)/r⌋+1 terms whose phases do not cancel exactly. The per-state bound 1/3r21/3r^21/3r2 requires a lower bound on such a sum that is uniform in rrr, ccc and kkk, with error terms of order 1/q1/q1/q controlled against a main term of order 1/r21/r^21/r2. The constant 1/31/31/3 leaves only a small margin below the limiting value 4/π2≈0.4054/\pi^2 \approx 0.4054/π2≈0.405, so the errors must be bounded explicitly, not merely shown to vanish.

The second difficulty is the counting: distinct coprime numerators ddd must give distinct outcomes ccc in [0,q)[0, q)[0,q), and each good ccc must determine rrr uniquely, which uses r<nr < nr<n and n2≤qn^2 \le qn2≤q.

Formalization scope

Conventions the statements commit to:

  • States are functions Fin q × ZMod n → ℂ; the matrix convention is row = input, so applying AqA_qAq​ to the first register gives the amplitude ∑aψ(a,y)(Aq)a,c\sum_a \psi(a, y)(A_q)_{a,c}∑a​ψ(a,y)(Aq​)a,c​ at (c,y)(c, y)(c,y).
  • The final state is built by applying AqA_qAq​ to the state (5.2); the closed forms (5.5) and (5.6) are theorems, not definitions. No normalization hypothesis is assumed.
  • Probabilities are squared moduli; the probability of the event "ccc gives us rrr" sums over all y∈Z/ny \in \mathbb{Z}/ny∈Z/n, which is exact because yyy that are not powers of xxx have probability zero.
  • xxx is a natural number with gcd⁡(x,n)=1\gcd(x, n) = 1gcd(x,n)=1; rrr is orderOf (x : ZMod n). qqq enters through the three hypotheses q=2lq = 2^lq=2l, n2≤qn^2 \le qn2≤q, q<2n2q < 2n^2q<2n2, not through a function of nnn.
  • Fractions are rationals, and "in lowest terms" is Rat.den.
  • Thresholds "for sufficiently large nnn" are ∃N, ∀n≥N\exists N,\ \forall n \ge N∃N, ∀n≥N, with NNN quantified before xxx, qqq, ccc and kkk.
  • Condition (5.11) is stated in its equivalent form (5.12), with an integer ddd.
  • Printed slip. Eq. (5.13)'s justification says "Because q>n2q > n^2q>n2", but qqq was chosen with n2≤qn^2 \le qn2≤q, and q=n2q = n^2q=n2 when nnn is a power of 222. The uniqueness claim holds under n2≤qn^2 \le qn2≤q, and that is what is stated.

Typing the closed form (5.4)–(5.6) in as the definition of the final state would make milestone 1 trivial and hide whether the probability model is the paper's; the definitions exclude this by construction.

Not stated: the polynomial running time of any step, the O(log⁡log⁡r)O(\log\log r)O(loglogr) repetition count (no explicit constant), the reversible modular exponentiation of §3, and the post-processing heuristics on p. 1501. Needed infrastructure: bounds on geometric exponential sums, Euler's totient, Diophantine approximation by fractions with bounded denominator, and Mathlib's continued fractions. Lemmas on geometric sums of roots of unity and on the order of units mod nnn are reusable in the companion discrete logarithm mission.

Selected references

  • P. W. Shor, Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer, SIAM J. Comput. 26(5):1484–1509, 1997. https://doi.org/10.1137/S0097539795293172
  • P. W. Shor, Algorithms for quantum computation: discrete logarithms and factoring, Proc. 35th FOCS, 1994. https://doi.org/10.1109/SFCS.1994.365700
  • G. L. Miller, Riemann's hypothesis and tests for primality, J. Comput. System Sci. 13(3):300–317, 1976. https://doi.org/10.1016/S0022-0000(76)80043-8
  • G. H. Hardy and E. M. Wright, An Introduction to the Theory of Numbers, 5th ed., Oxford, 1979 (Ch. X, continued fractions; Thm. 328).
  • M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information, Cambridge, 2000. https://doi.org/10.1017/CBO9780511976667
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Mathematical PhysicsTheoretical Computer Science·Captain: Lucas

Undecidability of the Spectral GapResearch Paper

Motivation

The spectral gap of a quantum many-body Hamiltonian is the difference between the energy of its ground state and the energy of its first excited state, in the limit of infinitely many particles. Whether a given microscopic interaction produces a gapped or a gapless system decides much of the macroscopic physics: gapped systems have exponentially decaying correlations and well-defined quantum phases, gapless systems sit at critical points and can display algebraically decaying correlations. Several long-standing questions — the Haldane conjecture for antiferromagnetic spin chains, the existence of gapped topological spin liquids, and the Yang–Mills mass gap — are instances of the question "given the interaction, is the system gapped?".

Cubitt, Pérez-García and Wolf proved that this question, posed for families of two-dimensional translationally invariant nearest-neighbour spin models, admits no algorithmic answer: the spectral gap problem is undecidable (Nature 528, 207–211 (2015); full version: Forum of Mathematics, Pi 10:e14 (2022), also arXiv:1502.04573).

Timeline of the ingredients the proof rests on: Turing's undecidability of the halting problem (1936); Berger's undecidability of the domino problem (1966) and Robinson's aperiodic tile set (Inventiones 12, 177–209 (1971)); Feynman's and Kitaev's circuit-to-Hamiltonian constructions, which turn a computation into a ground state; Gottesman and Irani's translationally invariant one-dimensional Hamiltonians encoding computation (FOCS 2009); and Bitansky–Vadhan-style quantum Turing machine engineering from Bernstein and Vazirani (SIAM J. Comput. 26, 1411–1473 (1997)). The 2015 result was later sharpened to one-dimensional chains by Bausch, Cubitt, Lucia and Pérez-García (PRX 10, 031038 (2020)).

Setting

Fix a local dimension ddd and, for each side length LLL, the square lattice Λ(L)={1,…,L}2\Lambda(L)=\{1,\dots,L\}^2Λ(L)={1,…,L}2 with open boundary conditions. Each site carries a copy of Cd\mathbb{C}^dCd, so the state space of the lattice has the standard product basis indexed by assignments of a level in {1,…,d}\{1,\dots,d\}{1,…,d} to each site. A model is specified by three Hermitian matrices: an on-site term h1h_1h1​ of size d×dd\times dd×d, and two interactions hrow,hcolh_{\mathrm{row}},h_{\mathrm{col}}hrow​,hcol​ of size d2×d2d^2\times d^2d2×d2 acting on horizontally and vertically adjacent pairs. The Hamiltonian of the finite lattice is

HΛ(L)  =  ∑horizontal edgeshrow(i,j)  +  ∑vertical edgeshcol(i,j)  +  ∑k∈Λ(L)h1(k),H^{\Lambda(L)} \;=\; \sum_{\text{horizontal edges}} h_{\mathrm{row}}^{(i,j)} \;+\; \sum_{\text{vertical edges}} h_{\mathrm{col}}^{(i,j)} \;+\; \sum_{k\in\Lambda(L)} h_1^{(k)},HΛ(L)=horizontal edges∑​hrow(i,j)​+vertical edges∑​hcol(i,j)​+k∈Λ(L)∑​h1(k)​,

the same three matrices being used at every edge and every site, which is what translational invariance means here. The quantity max⁡{∥h1∥,∥hrow∥,∥hcol∥}\max\{\|h_1\|,\|h_{\mathrm{row}}\|,\|h_{\mathrm{col}}\|\}max{∥h1​∥,∥hrow​∥,∥hcol​∥} is the local interaction strength.

Write λ0(HΛ(L))≤λ1(HΛ(L))≤⋯\lambda_0(H^{\Lambda(L)})\le\lambda_1(H^{\Lambda(L)})\le\cdotsλ0​(HΛ(L))≤λ1​(HΛ(L))≤⋯ for the eigenvalues and Δ(HΛ(L))=λ1−λ0\Delta(H^{\Lambda(L)})=\lambda_1-\lambda_0Δ(HΛ(L))=λ1​−λ0​ for the finite-size gap. The family {HΛ(L)}L\{H^{\Lambda(L)}\}_L{HΛ(L)}L​ is

  • gapped (Definition 1 of the source) if there are γ>0\gamma>0γ>0 and L0L_0L0​ such that for all L>L0L>L_0L>L0​ the ground state of HΛ(L)H^{\Lambda(L)}HΛ(L) is non-degenerate and Δ(HΛ(L))≥γ\Delta(H^{\Lambda(L)})\ge\gammaΔ(HΛ(L))≥γ;
  • gapless (Definition 2 of the source) if there is c>0c>0c>0 such that for every ε>0\varepsilon>0ε>0 there is an L0L_0L0​ with: for all L>L0L>L_0L>L0​, every point of [λ0,λ0+c][\lambda_0,\lambda_0+c][λ0​,λ0​+c] lies within ε\varepsilonε of the spectrum of HΛ(L)H^{\Lambda(L)}HΛ(L).

These two conditions are not negations of each other; the construction guarantees that every instance falls into one of them. The ground state energy density is Eρ=lim⁡L→∞λ0(HΛ(L))/L2E_\rho=\lim_{L\to\infty}\lambda_0(H^{\Lambda(L)})/L^2Eρ​=limL→∞​λ0​(HΛ(L))/L2.

Formalization targets

Goal — Theorem 3 of the source

For a fixed universal machine and every nnn, one explicit family of interactions, built from fixed integer-valued matrices A,A′,B,C,D,D′A,A',B,C,D,D'A,A′,B,C,D,D′, a diagonal projector Π\PiΠ, a rational β>0\beta>0β>0 that may be taken arbitrarily small, and an algebraic α(n)≤2β\alpha(n)\le 2\betaα(n)≤2β,

h1(n)=α(n)Π,hcol(n)=D+βD′,h_1(n)=\alpha(n)\Pi,\qquad h_{\mathrm{col}}(n)=D+\beta D',h1​(n)=α(n)Π,hcol​(n)=D+βD′, hrow(n)=A+β(A′+eiπφB+e−iπφB†+eiπ2−∣φ∣C+e−iπ2−∣φ∣C†),h_{\mathrm{row}}(n)=A+\beta\Bigl(A'+e^{i\pi\varphi}B+e^{-i\pi\varphi}B^{\dagger}+e^{i\pi 2^{-|\varphi|}}C+e^{-i\pi 2^{-|\varphi|}}C^{\dagger}\Bigr),hrow​(n)=A+β(A′+eiπφB+e−iπφB†+eiπ2−∣φ∣C+e−iπ2−∣φ∣C†),

with φ=φ(n)\varphi=\varphi(n)φ=φ(n) the rational whose binary expansion after the point is the binary expansion of nnn reversed, satisfies: the local interaction strength is at most 111; if the machine halts on input nnn the family is gapped with gap at least 111; and if it does not halt the family is gapless. Since halting is undecidable, no algorithm decides gappedness, even with the promise that exactly one of the two alternatives holds and even at fixed local dimension ddd.

Milestones

The milestone list follows the numbering of the full version: Lemma 8 and Theorem 9 (reduction of halting to ground state energy and to arbitrary low-energy properties), Corollary 7 (the same undecidability for unconstrained local dimension, with rational interactions), Proposition 53 and Corollary 54 (the diverging ground state energy and its promise version), and Theorem 5 (undecidability of the ground state energy density).

Significance

The result rules out a general algorithm — and therefore any complete general method — for deciding gappedness from the interaction matrices, however much computing power is available; the property genuinely depends on arbitrarily large system sizes. It also implies, via the standard link between undecidability and independence, that there are concrete finite-dimensional models whose gap is independent of the axioms of any consistent recursively axiomatized formal system (Corollary 4 of the source), and it transfers to other low-energy properties such as the existence of algebraically decaying ground-state correlations.

The theorem is proved; none of it is formalized. This mission produces the machine-checked version. The reusable infrastructure it forces into existence is substantial on its own: a formal model of translationally invariant lattice Hamiltonians and their thermodynamic-limit spectral behaviour, the tiling layer, and computational-history-state Hamiltonians. Each milestone is a self-contained statement that can be attacked without the others.

Difficulty

The obvious approach — encode a halting computation as an energy penalty — gives the ground state energy of a finite lattice, not a property of the limit; this is exactly what Lemma 8 achieves, and it is not enough, because a gap is a statement about the sequence of spectra as L→∞L\to\inftyL→∞ and is insensitive to any single lattice size. The construction must make the halting information visible at all sufficiently large sizes at once while a fixed finite local dimension carries every instance nnn. That forces three separate difficulties: an aperiodic (Robinson) tiling to create squares of every size 2n2^n2n inside one translationally invariant model; a quantum phase-estimation Turing machine whose transition amplitudes encode nnn in a single phase eiπφ(n)e^{i\pi\varphi(n)}eiπφ(n), so that the instance index does not inflate the local dimension; and a history-state Hamiltonian whose low-energy spectrum can be controlled well enough that a positive energy density in the halting case turns into a genuine spectral gap, and a vanishing one into a dense spectrum above the ground state.

Formalization scope

The development commits to the following conventions, all of which are visible in the definition items of this mission.

  1. Lattices are finite: sites are pairs of indices in {0,…,L−1}\{0,\dots,L-1\}{0,…,L−1}, edges are consecutive pairs within a row or a column (open boundary conditions; the periodic case of Section 6.3 of the source is out of scope).
  2. Operators are complex matrices indexed by product-basis configurations; the interactions are embedded by acting as the given matrix on the two sites of an edge and as the identity elsewhere.
  3. The spectrum is taken as the set of real numbers in the matrix spectrum, and λ0\lambda_0λ0​ is its infimum; every statement carries the Hermiticity hypotheses that make this the usual spectrum. Multiplicities are dimensions of eigenspaces, which is how the "identity of spectra as multisets" of Theorem 9 is expressed.
  4. Gapped, gapless and the energy density are properties of the whole family {HΛ(L)}L\{H^{\Lambda(L)}\}_L{HΛ(L)}L​ generated by a fixed triple of matrices, exactly as in Definitions 1 and 2.
  5. Operator norms are ℓ2\ell_2ℓ2​ operator norms; the local interaction strength is the maximum of the three.
  6. Machines are represented by partial recursive codes: "halts on input nnn" is definedness of the evaluation, and "has not halted after LLL steps" is the step-bounded evaluation returning nothing. The explicit local-dimension bounds of Lemma 8 and Theorem 9, which are stated in the source in terms of the number of internal states and the alphabet size of a Turing machine, are replaced by the existence of a finite local dimension.

Degenerate readings are excluded: a zero local dimension satisfies none of the statements, since a non-degenerate ground state requires a one-dimensional eigenspace and the gapless condition requires a non-empty spectrum; and every existential statement fixes the matrices before quantifying over all instances nnn and all lattice sizes LLL.

Contributions are welcome at any milestone, and also on the infrastructure the milestones need — Wang tilings and the Robinson tile set, Gottesman–Irani history-state Hamiltonians, and quantum Turing machines in the Bernstein–Vazirani sense — which are needed for Theorem 6 and Lemma 47 of the source and are not yet part of this mission's item list.

Selected references

  • T. S. Cubitt, D. Pérez-García, M. M. Wolf, Undecidability of the Spectral Gap (full version), Forum of Mathematics, Pi 10:e14, 1–102 (2022). https://doi.org/10.1017/fmp.2021.15 — the version all statements of this mission are formalized against; preprint: https://arxiv.org/abs/1502.04573
  • T. S. Cubitt, D. Pérez-García, M. M. Wolf, Undecidability of the spectral gap, Nature 528, 207–211 (2015). https://doi.org/10.1038/nature16059
  • R. M. Robinson, Undecidability and nonperiodicity for tilings of the plane, Inventiones Mathematicae 12, 177–209 (1971). https://doi.org/10.1007/BF01418780
  • D. Gottesman, S. Irani, The quantum and classical complexity of translationally invariant tiling and Hamiltonian problems, FOCS 2009. https://arxiv.org/abs/0905.2419
  • E. Bernstein, U. Vazirani, Quantum complexity theory, SIAM J. Comput. 26, 1411–1473 (1997). https://doi.org/10.1137/S0097539796300921
  • J. Bausch, T. S. Cubitt, A. Lucia, D. Pérez-García, Undecidability of the spectral gap in one dimension, Phys. Rev. X 10, 031038 (2020). https://doi.org/10.1103/PhysRevX.10.031038
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Captain: Henry Yuen

Parallel repetition for quantum gamesResearch Paper

Parallel repetition for quantum games

Nonlocal games

A nonlocal game is played between a classical referee and two or more cooperating players who are not allowed to communicate during the game. In the two-player, one-round setting, the referee samples a pair of questions (x,y)(x,y)(x,y) from a distribution μ\muμ, sends xxx to Alice and yyy to Bob, and receives answers aaa and bbb. The players win when a predicate V(x,y,a,b)V(x,y,a,b)V(x,y,a,b) accepts. Before the game begins they may agree on a strategy and share a resource, but after receiving their questions they are isolated from one another.

Nonlocal games occupy a useful interface between complexity theory and quantum information. From the perspective of complexity theory, they are the basic objects underlying multiprover interactive proofs: a verifier delegates a computation to separated provers and uses the consistency of their answers to distinguish valid from invalid claims. Classical two-prover games play a central role in the PCP theorem, hardness of approximation, and soundness amplification. Allowing the provers to share entanglement leads to the class MIP∗\mathrm{MIP}^*MIP∗ and to a substantially richer theory. The theorem MIP∗=RE\mathrm{MIP}^*=\mathrm{RE}MIP∗=RE shows how dramatically entanglement changes this landscape: even estimating the entangled value of a nonlocal game can encode undecidable computation Ji--Natarajan--Vidick--Wright--Yuen 2020.

From the perspective of quantum information, nonlocal games are operational formulations of Bell experiments. A separation between classical and entangled values witnesses correlations that cannot be explained by a local hidden-variable model. The same framework supports self-testing, in which near-optimal behavior certifies the underlying state and measurements up to local equivalence, and device-independent cryptography, in which security or randomness is certified from observed input-output statistics rather than a trusted description of the devices. Representative references include Cleve--Høyer--Toner--Watrous 2004, Reichardt--Unger--Vazirani 2013, and Pironio et al. 2010. The survey of Palazuelos--Vidick 2016 describes further connections among nonlocal games, Bell inequalities, operator spaces, and quantum information.

Thus the value of a nonlocal game is simultaneously a complexity-theoretic soundness parameter and a quantitative measure of the power of nonclassical correlations. Understanding how this value changes under natural operations on games is important in both subjects.

Entangled strategies and value

We take the finite answer alphabets to be nonempty. In a classical strategy, Alice's answer depends only on xxx, Bob's answer depends only on yyy, and the players may coordinate using shared randomness. In a finite-dimensional entangled strategy, the players share a bipartite state ρ\rhoρ and use POVM measurement operators

{Aax}a∈Aand{Bby}b∈B\{A_a^x\}_{a\in A} \qquad\text{and}\qquad \{B_b^y\}_{b\in B}{Aax​}a∈A​and{Bby​}b∈B​

for their respective questions. The probability of producing answers (a,b)(a,b)(a,b) on questions (x,y)(x,y)(x,y) is

Re⁡Tr⁡ ⁣(ρ (Aax⊗Bby)).\operatorname{Re}\operatorname{Tr}\!\left(\rho\,(A_a^x\otimes B_b^y)\right).ReTr(ρ(Aax​⊗Bby​)).

The supremum of the winning probability over all such finite-dimensional strategies is the entangled value ω∗(G)\omega^*(G)ω∗(G). This optimization ranges over arbitrary local dimensions, shared states, and local measurements, which is one reason even apparently elementary questions about nonlocal games can be difficult.

Parallel repetition

For a positive integer nnn, the repeated game GnG^nGn consists of nnn independently sampled copies of GGG played simultaneously. Alice receives (x1,…,xn)(x_1,\ldots,x_n)(x1​,…,xn​), Bob receives (y1,…,yn)(y_1,\ldots,y_n)(y1​,…,yn​), and they answer with tuples (a1,…,an)(a_1,\ldots,a_n)(a1​,…,an​) and (b1,…,bn)(b_1,\ldots,b_n)(b1​,…,bn​). They win only if

V(xi,yi,ai,bi)=1V(x_i,y_i,a_i,b_i)=1V(xi​,yi​,ai​,bi​)=1

for every coordinate iii.

Parallel repetition is a basic method of soundness amplification. Starting from a game that dishonest players cannot win with certainty, the verifier repeats the test in the hope of driving the optimal success probability rapidly toward zero. The difficulty is that independence in the verifier's sampling does not force independence in the players' strategy. Alice may choose her entire answer tuple as a function of all her questions, Bob may do the same, and an entangled strategy may use a single state and joint measurements spanning all coordinates. In particular, one cannot obtain an upper bound on ω∗(Gn)\omega^*(G^n)ω∗(Gn) merely by analyzing the strategy that plays each coordinate independently.

For classical games, Raz's parallel repetition theorem gives exponential decay whenever the one-shot value is below one Raz 1998. Establishing the corresponding behavior for entangled games has been a long-running problem. A general polynomial bound was proved in Yuen 2016, implying for the first time that ω∗(Gn)\omega^*(G^n)ω∗(Gn) tends to zero for every finite two-player entangled game with ω∗(G)<1\omega^*(G)<1ω∗(G)<1.

The full exponential-decay theorem was recently settled by OpenAI. In Chapter 6 of Ten Advances in Mathematics and Theoretical Computer Science, OpenAI proves that for every finite two-player entangled game GGG with ω∗(G)<1\omega^*(G)<1ω∗(G)<1, there is a constant cG>0c_G>0cG​>0 such that

ω∗(Gn)≤e−cGn\omega^*(G^n)\le e^{-c_G n}ω∗(Gn)≤e−cG​n

for every positive nnn. OpenAI also released a Lean certificate for the result. This resolves the general quantum parallel-repetition conjecture, but it does not end the study of the problem. The proof introduces quantitative losses and a substantial technical apparatus, and there remains considerable value in finding alternative arguments, isolating the essential mechanism, improving the dependence on the one-shot gap and answer size, and producing shorter or more conceptual formal proofs.

A hierarchy of formalization targets

This mission develops a reusable Lean framework for parallel repetition rather than formalizing only one paper. Its targets are organized by the strength of the asserted decay.

Qualitative decay

The main mission theorem is the fundamental asymptotic statement:

ω∗(G)<1⟹lim⁡n→∞ω∗(Gn)=0.\omega^*(G)<1 \quad\Longrightarrow\quad \lim_{n\to\infty}\omega^*(G^n)=0.ω∗(G)<1⟹n→∞lim​ω∗(Gn)=0.

Equivalently, for every δ>0\delta>0δ>0, all sufficiently large nnn satisfy ω∗(Gn)<δ\omega^*(G^n)<\deltaω∗(Gn)<δ. This statement deliberately specifies no rate. It is a stable top-level theorem that can be recovered from any sufficiently strong quantitative bound.

Polynomial decay

A stronger target asks for game-dependent constants C>0C>0C>0 and α>0\alpha>0α>0 such that

ω∗(Gn)≤Cn−α.\omega^*(G^n)\le Cn^{-\alpha}.ω∗(Gn)≤Cn−α.

The abstract formulation avoids fixing a particular exponent or logarithmic correction. More refined formalizations can record explicit dependence on the gap 1−ω∗(G)1-\omega^*(G)1−ω∗(G), the answer alphabet, or other game parameters. Yuen's 2016 theorem is one important result at this level.

Exponential decay

The exponential target asks for game-dependent constants C,c>0C,c>0C,c>0 such that

ω∗(Gn)≤Ce−cn.\omega^*(G^n)\le C e^{-cn}.ω∗(Gn)≤Ce−cn.

Following OpenAI's recent resolution, this target is now a theorem rather than an open conjecture. Within this mission it remains a central milestone: contributors may formalize the released argument in the mission's common interface, construct an independent proof, seek a more elegant or modular proof, or establish sharper quantitative variants.

These levels do not exhaust the project. The same framework can accommodate explicit finite-nnn inequalities, stretched-exponential estimates, bounds for structured classes of games, improved parameter dependence, and reductions showing that one decay statement implies another.

Formalization scope

The foundational Lean development represents a game by finite question sets X,YX,YX,Y, finite answer sets A,BA,BA,B, a nonnegative normalized question distribution μ(x,y)\mu(x,y)μ(x,y), and a Boolean verification predicate V(x,y,a,b)V(x,y,a,b)V(x,y,a,b). The parallel-repetition theorems explicitly assume that AAA and BBB are nonempty. The development defines finite-dimensional entangled strategies using density matrices and POVM measurement operators, defines the repeated game on tuples, and takes the entangled value as a supremum over all finite-dimensional strategies. Repeated strategies are indexed by complete question tuples and are not required to factor coordinatewise.

A complete development will draw on formal libraries for finite probability, tensor products, positive semidefinite matrices, density matrices, POVMs, trace norms, fidelity, entropy, mutual information, and correlated sampling. These components should be formulated for reuse and should expose the dependence of each bound on the relevant game parameters.

The goal is both to verify parallel-repetition theorems and to build a dependable language for nonlocal games in Lean. Formalization forces distinctions that are easy to suppress on paper: whether constants depend on the game, whether a bound holds for all nnn or only asymptotically, which strategy model is optimized over, and which hypotheses are needed for a particular rate. The mission welcomes reconstructions of known proofs as well as new, shorter, or conceptually different proofs.

Selected references

  • R. Cleve, P. Høyer, B. Toner, and J. Watrous, Consequences and limits of nonlocal strategies, CCC 2004.
  • R. Raz, A parallel repetition theorem, SIAM Journal on Computing 27(3), 1998.
  • H. Yuen, A parallel repetition theorem for all entangled games, ICALP 2016.
  • Z. Ji, A. Natarajan, T. Vidick, J. Wright, and H. Yuen, MIP∗=RE\mathrm{MIP}^*=\mathrm{RE}MIP∗=RE, Communications of the ACM 64(11), 2021.
  • OpenAI, Ten Advances in Mathematics and Theoretical Computer Science, Chapter 6, 2026; accompanying Lean formalization.
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Machine LearningTheoretical Computer Science·Captain: mikedeng1

Shadow Tomography of Quantum States 1: Polylogarithmically Many Copies Suffice to Estimate Every Acceptance Probability to Within εResearch Paper

Motivation

Learning an unknown quantum state is expensive. Full quantum state tomography of a DDD-dimensional mixed state ρ\rhoρ to accuracy ε\varepsilonε in trace distance needs on the order of D2/ε2D^2/\varepsilon^2D2/ε2 copies of ρ\rhoρ (O'Donnell–Wright 2016; Haah et al. 2017), and this is optimal. For a system of nnn qubits, D=2nD = 2^nD=2n, so full tomography is out of reach beyond a few dozen qubits.

Often one does not need the whole density matrix, only the behaviour of ρ\rhoρ on a fixed list of tests: acceptance probabilities of verification circuits, expectation values of observables, or the answers a piece of quantum advice gives to a set of questions. Aaronson (arXiv:1711.01053, STOC 2018) named this task shadow tomography and asked whether the number of copies can be polylogarithmic in both the dimension and the number of tests. Measuring each test on separate copies costs O~(M/ε2)\tilde O(M/\varepsilon^2)O~(M/ε2) copies, which is linear in MMM.

Timeline.

  • 2016: the question was posed at a mini-course without a name (Aaronson, The Complexity of Quantum States and Transformations, §8.3.1).
  • 2016: Harrow, Lin and Montanaro gave a correct "quantum OR" test, repairing an earlier flawed claim (arXiv:1607.03236, Corollary 11).
  • 2017–2018: Aaronson proved the first polylogarithmic bound, the theorem of this mission.
  • Later work improved the exponents, notably Bădescu–O'Donnell 2021, and introduced the related "classical shadows" of Huang–Kueng–Preskill 2020.

Setting

A mixed state of dimension DDD is a D×DD\times DD×D Hermitian positive semidefinite matrix ρ\rhoρ with Tr ρ=1\mathrm{Tr}\,\rho = 1Trρ=1. A two-outcome measurement is a D×DD\times DD×D Hermitian matrix EEE with all eigenvalues in [0,1][0,1][0,1]. Equivalently, 0⪯E⪯10 \preceq E \preceq \mathbb 10⪯E⪯1. It accepts ρ\rhoρ with probability Tr(Eρ)\mathrm{Tr}(E\rho)Tr(Eρ).

The state ρ⊗k\rho^{\otimes k}ρ⊗k consists of kkk independent copies of ρ\rhoρ. A measurement of ρ⊗k\rho^{\otimes k}ρ⊗k with classical output is a POVM: a finite family of positive semidefinite matrices PωP_\omegaPω​ on the kkk-register space with ∑ωPω=1\sum_\omega P_\omega = \mathbb 1∑ω​Pω​=1. Outcome ω\omegaω occurs with probability Tr(Pωρ⊗k)\mathrm{Tr}(P_\omega\rho^{\otimes k})Tr(Pω​ρ⊗k). An adaptive procedure that measures the copies one after another is described by one such POVM.

Problem 1 (shadow tomography). Given an unknown ρ\rhoρ and known two-outcome measurements E1,…,EME_1,\dots,E_ME1​,…,EM​, output numbers b1,…,bM∈[0,1]b_1,\dots,b_M\in[0,1]b1​,…,bM​∈[0,1] with ∣bi−Tr(Eiρ)∣≤ε|b_i-\mathrm{Tr}(E_i\rho)|\le\varepsilon∣bi​−Tr(Ei​ρ)∣≤ε for all iii, with success probability at least 1−δ1-\delta1−δ. The output must come from a measurement of ρ⊗k\rho^{\otimes k}ρ⊗k, with k=k(D,M,ε,δ)k=k(D,M,\varepsilon,\delta)k=k(D,M,ε,δ) as small as possible. The measurement may depend on the EiE_iEi​, but not on ρ\rhoρ.

Formalization targets

Goal: Theorem 2, in the explicit form proved in §5

There is a universal constant CCC such that, for M≥2M\ge2M≥2 and 0<ε,δ≤1/20<\varepsilon,\delta\le 1/20<ε,δ≤1/2, Problem 1 is solvable with

k≤C log⁡Dε(log⁡log⁡D+log⁡1εε2)2log⁡4M(log⁡log⁡M+log⁡log⁡D+log⁡1ε+log⁡1δ)=O~(log⁡1/δε5log⁡4Mlog⁡D)k \le C\,\frac{\log D}{\varepsilon}\Big(\frac{\log\log D+\log\frac1\varepsilon}{\varepsilon^{2}}\Big)^{2}\log^4 M\Big(\log\log M+\log\log D+\log\frac1\varepsilon+\log\frac1\delta\Big) = \tilde O\Big(\frac{\log 1/\delta}{\varepsilon^5}\log^4 M\log D\Big)k≤CεlogD​(ε2loglogD+logε1​​)2log4M(loglogM+loglogD+logε1​+logδ1​)=O~(ε5log1/δ​log4MlogD)

copies. This is the last display of the proof (p. 19). The goal fixes no constant, so any improvement of CCC remains consistent with it.

Milestones

  • Theorem 13 (Harrow–Lin–Montanaro). A one-copy test that accepts with probability at least (1−ϵ)2/7(1-\epsilon)^2/7(1−ϵ)2/7 if some Tr(Eiρ)≥1−ϵ\mathrm{Tr}(E_i\rho)\ge1-\epsilonTr(Ei​ρ)≥1−ϵ, and at most 4ΔM4\Delta M4ΔM if ∑iTr(Eiρ)≤ΔM\sum_i\mathrm{Tr}(E_i\rho)\le\Delta M∑i​Tr(Ei​ρ)≤ΔM.
  • Lemma 14 (Quantum OR Bound). Deciding whether max⁡iTr(Eiρ)≥c\max_i\mathrm{Tr}(E_i\rho)\ge cmaxi​Tr(Ei​ρ)≥c or ≤c−ε\le c-\varepsilon≤c−ε with O(log⁡(1/δ)log⁡M/ε2)O(\log(1/\delta)\log M/\varepsilon^2)O(log(1/δ)logM/ε2) copies, independent of DDD.
  • Lemma 15 (Gentle Search). Finding jjj with Tr(Ejρ)≥c−ε\mathrm{Tr}(E_j\rho)\ge c-\varepsilonTr(Ej​ρ)≥c−ε with O(log⁡4Mε2(log⁡log⁡M+log⁡1δ))O(\frac{\log^4M}{\varepsilon^2}(\log\log M+\log\frac1\delta))O(ε2log4M​(loglogM+logδ1​)) copies.
  • Amplification claims (p. 16). The threshold tests Ei,t,±∗E^*_{i,t,\pm}Ei,t,±∗​ on ρ⊗q\rho^{\otimes q}ρ⊗q accept with probability at least 5/65/65/6 when the hypothesis is off by ε\varepsilonε, and at most 1/31/31/3 when it is within ε/2\varepsilon/2ε/2.
  • Markov claim (p. 17). The postselection test FtF_tFt​ on an arbitrary, possibly entangled, qqq-register state accepts with probability at most aq(a+ε/4)q\frac{a q}{(a+\varepsilon/4)q}(a+ε/4)qaq​.
  • Lemma 12 (Quantum Union Bound, probability part). Measurements each accepting with probability at least 1−ε1-\varepsilon1−ε all accept in succession with probability at least 1−2Mε1-2M\sqrt\varepsilon1−2Mε​.
  • Chernoff claim (p. 18). 1−Tr(Ftρ⊗q)≤ε4/log⁡2D1-\mathrm{Tr}(F_t\rho^{\otimes q})\le\varepsilon^4/\log^2D1−Tr(Ft​ρ⊗q)≤ε4/log2D.
  • Proposition 20. Promise-gap thresholds for all iii at once can be decided with O(log⁡(M/δ)/ε2)O(\log(M/\delta)/\varepsilon^2)O(log(M/δ)/ε2) copies.

Significance

The result. Theorem 2 shows that a state of exponential dimension can be learned "for all practical purposes" on exponentially many tests from polynomially many copies. Applications in the paper include a bound on quantum advice and one-way communication, and implications for quantum money and copy-protection. It also shows that the information needed to predict many measurement outcomes is far smaller than the description of ρ\rhoρ.

Formalizing it. The theorem is proved in the paper, and later work improves its exponents. As far as is known it has not been machine-checked. A complete development formalizes the gentle-measurement toolkit (Lemma 12, Lemma 14, Lemma 15), the amplification of two-outcome measurements on tensor powers, and the postselection argument. These are standard tools of quantum learning theory and quantum complexity with no formal counterpart yet. Lemma 14 and Lemma 15 are reusable beyond this mission.

Difficulty

The naive approach measures the EiE_iEi​ directly on shared copies. A measurement that is likely to reject disturbs the state, so later measurements see a damaged state, and separate copies per measurement cost MMM copies.

The proof needs three ingredients:

  • a gentle search that finds a measurement on which the current hypothesis is wrong while damaging the copies only slightly;
  • a potential argument showing that postselection cannot happen too often;
  • a uniform control of the damage.

The potential argument has to hold for the state after postselection, which is correlated or entangled across registers. Independence-based concentration fails there, which is why the Markov claim, not a Chernoff bound, governs that step. Theorem 13 itself rests on a delicate ancilla-based procedure of Harrow, Lin and Montanaro, and the mission cites it as a milestone without its proof.

Formalization scope

  • Representation.
    • Operators are complex matrices over a finite index type, and states use the published WildeQIT.IsDensityOperator (positive semidefinite, trace one).
    • A two-outcome measurement is IsEffect E: both EEE and 1−E\mathbb 1-E1−E are positive semidefinite.
    • ρ⊗k\rho^{\otimes k}ρ⊗k is a matrix indexed by kkk-tuples Fin k → n.
    • A measurement with output is a POVM structure with a finite outcome type. Probabilities are real parts of traces.
  • Quantifier order of the goal. ∃C\exists C∃C, then for all D,M,ε,δD,M,\varepsilon,\deltaD,M,ε,δ there is kkk; then for all EiE_iEi​ there are a POVM and outputs bbb; then for all ρ\rhoρ. Choosing the measurement after ρ\rhoρ would make the goal trivial (output the true values with k=0k=0k=0), and this order rules that out.
  • Disclosed hypotheses.
    • Theorem 2 assumes M≥2M\ge2M≥2, ε≤1/2\varepsilon\le1/2ε≤1/2 and δ≤1/2\delta\le1/2δ≤1/2. These keep the logarithmic factors positive; at M=1M=1M=1 the bound would force k=0k=0k=0.
    • Lemma 14 assumes M≥2M\ge2M≥2, and Lemmas 14 and 15 bound δ\deltaδ.
    • Theorem 13 assumes ϵ≤1/2\epsilon\le1/2ϵ≤1/2, as in Harrow–Lin–Montanaro's Corollary 11.
    • The Chernoff claim assumes D≥2D\ge2D≥2.
  • Conventions.
    • All logarithms are natural, including inside log⁡log⁡\log\logloglog.
    • Amplified tests use real thresholds.
    • "Applied in succession" in Lemma 12 uses Lüders instruments (E\sqrt{E}E​ Kraus operators), in the order E1,E2,…E_1,E_2,\dotsE1​,E2​,….
    • The hypothesis ρt\rho_tρt​ enters the amplification claims only as the number a=Tr(Eρt)a=\mathrm{Tr}(E\rho_t)a=Tr(Eρt​).
  • Printed steps not drafted.
    • The printed ε−4\varepsilon^{-4}ε−4 form of Theorem 2 relies on an external online-learning algorithm that is only sketched.
    • The halting rule of §5 is unspecified, because Lemma 15 always returns an index.
    • The asymptotic claims pt≥0.9/Dqp_t\ge0.9/D^qpt​≥0.9/Dq for t=o(log⁡2D/ε4)t=o(\log^2D/\varepsilon^4)t=o(log2D/ε4) and t=O(qlog⁡D/ε)t=O(q\log D/\varepsilon)t=O(qlogD/ε) use a circular o(⋅)o(\cdot)o(⋅).
    • The trace-distance part of Lemma 12 has an unquantified O(⋅)O(\cdot)O(⋅).
    • Lemma 12's printed bound 1−2Mε1-2M\sqrt\varepsilon1−2Mε​ is weaker than its use on p. 18. It is stated as printed. The proof of the goal must retune constants or use Wilde's stronger 1−2Mε1-2\sqrt{M\varepsilon}1−2Mε​-type bound.
  • Contributions welcome. Proofs of any milestone; a formal Hoeffding bound for binomial counts of product effects; the gentle measurement lemma for Lüders instruments; Naimark dilation for effects.

Selected references

  • S. Aaronson, Shadow Tomography of Quantum States, STOC 2018; arXiv:1711.01053v2, 2018. https://arxiv.org/abs/1711.01053
  • A. W. Harrow, C. Y.-Y. Lin, A. Montanaro, Sequential measurements, disturbance and property testing, SODA 2017. https://arxiv.org/abs/1607.03236
  • M. M. Wilde, Sequential decoding of a general classical-quantum channel, Proc. R. Soc. A, 2013. https://arxiv.org/abs/1303.0808
  • R. O'Donnell, J. Wright, Efficient quantum tomography, STOC 2016. https://arxiv.org/abs/1508.01907
  • J. Haah, A. W. Harrow, Z. Ji, X. Wu, N. Yu, Sample-optimal tomography of quantum states, IEEE Trans. Inf. Theory, 2017. https://arxiv.org/abs/1508.01797
  • C. Bădescu, R. O'Donnell, Improved quantum data analysis, STOC 2021. https://arxiv.org/abs/2011.10908
  • H.-Y. Huang, R. Kueng, J. Preskill, Predicting many properties of a quantum system from very few measurements, Nature Physics, 2020. https://arxiv.org/abs/2002.08953
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Number TheoryProbabilityTheoretical Computer Science·Captain: mikedeng1

Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer 4: The Discrete Logarithm Circuit Gives a Good Output with Probability at Least 1/480Research Paper

Motivation

The discrete logarithm problem modulo a prime asks, given a prime ppp, a generator ggg of the multiplicative group modulo ppp, and a nonzero residue xxx, for the exponent rrr with gr≡x(modp)g^r\equiv x \pmod pgr≡x(modp). Its presumed classical hardness underlies Diffie–Hellman key exchange, ElGamal encryption and the Digital Signature Algorithm. The best classical algorithm known when Shor wrote, Gordon's adaptation of the number field sieve, runs in time exp⁡(O((log⁡p)1/3(log⁡log⁡p)2/3))\exp(O((\log p)^{1/3}(\log\log p)^{2/3}))exp(O((logp)1/3(loglogp)2/3)).

In §6 of Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer (SIAM J. Comput. 26(5), 1997; doi:10.1137/S0097539795293172, arXiv:quant-ph/9508027), Shor gave a quantum algorithm that uses two modular exponentiations and two quantum Fourier transforms and outputs, with constant probability, a pair from which rrr can be computed. The quantitative core of that analysis is a single number: the circuit produces a "good" output with probability at least 1/4801/4801/480. This mission formalizes that bound and the three estimates it is assembled from.

Setting

Let ppp be a prime and ggg a generator of (Z/pZ)×(\mathbb Z/p\mathbb Z)^\times(Z/pZ)×, so that 1,g,…,gp−21,g,\dots,g^{p-2}1,g,…,gp−2 are all the nonzero residues. Fix the unknown rrr with 0≤r<p−10\le r<p-10≤r<p−1 and put x=grx=g^rx=gr. Let q=2lq=2^lq=2l be the power of 222 with p<q<2pp<q<2pp<q<2p.

The Fourier matrix AqA_qAq​ is the q×qq\times qq×q matrix with entries (Aq)a,c=q−1/2exp⁡(2πi ac/q)(A_q)_{a,c}=q^{-1/2}\exp(2\pi i\,ac/q)(Aq​)a,c​=q−1/2exp(2πiac/q) for 0≤a,c<q0\le a,c<q0≤a,c<q (§4, eq. (4.1)). Rows index input basis vectors and columns output basis vectors.

The algorithm uses three registers: two holding numbers 0≤a,b<q0\le a,b<q0≤a,b<q and one holding a nonzero residue modulo ppp. It starts from the state

1p−1∑a=0p−2∑b=0p−2∣a,b,gax−b (mod p)⟩(6.1)\frac{1}{p-1}\sum_{a=0}^{p-2}\sum_{b=0}^{p-2}|a,b,g^ax^{-b}\ (\mathrm{mod}\ p)\rangle \qquad (6.1)p−11​a=0∑p−2​b=0∑p−2​∣a,b,gax−b (mod p)⟩(6.1)

(preFourierState), applies AqA_qAq​ to each of the first two registers (finalState), and measures all three registers. The probability of observing ∣c,d,y⟩|c,d,y\rangle∣c,d,y⟩ is the squared modulus of its amplitude (outcomeProb).

For integers zzz and q>0q>0q>0, the symmetric residue {z}q\{z\}_q{z}q​ is the residue of zzz modulo qqq in (−q/2,q/2](-q/2,q/2](−q/2,q/2] (symmRes). Put

T=rc+d−rp−1{c(p−1)}q.T=rc+d-\frac{r}{p-1}\{c(p-1)\}_q .T=rc+d−p−1r​{c(p−1)}q​.

An observed state ∣c,d,y⟩|c,d,y\rangle∣c,d,y⟩ is good (IsGood) when

∣{T}q∣≤12(6.10)and∣{c(p−1)}q∣≤q/12(6.11).|\{T\}_q|\le\tfrac12 \quad (6.10) \qquad\text{and}\qquad |\{c(p-1)\}_q|\le q/12 \quad (6.11).∣{T}q​∣≤21​(6.10)and∣{c(p−1)}q​∣≤q/12(6.11).

Goodness depends only on (c,d)(c,d)(c,d).

Formalization targets

Goal: a good output with probability at least 1/4801/4801/480 (§6, p. 1504)

∑0≤c,d<q(c,d) good ∑y∈(Z/p)×Pr⁡[c,d,y] ≥ 1480.\sum_{\substack{0\le c,d<q\\ (c,d)\ \text{good}}}\ \sum_{y\in(\mathbb Z/p)^\times}\Pr[c,d,y]\ \ge\ \frac1{480}.0≤c,d<q(c,d) good​∑​ y∈(Z/p)×∑​Pr[c,d,y] ≥ 4801​.

The constant is the one the page carries forward. The goal fixes no threshold on ppp: it is stated for every prime ppp that admits a power of two strictly between ppp and 2p2p2p.

Milestones

  1. The output distribution, eq. (6.4). For 0≤k<p−10\le k<p-10≤k<p−1,
Pr⁡[c,d,gk]=∣1(p−1)q∑0≤a,b≤p−2a−rb≡k (p−1)exp⁡(2πiq(ac+bd))∣2.\Pr[c,d,g^k]=\left|\frac{1}{(p-1)q}\sum_{\substack{0\le a,b\le p-2\\ a-rb\equiv k\ (p-1)}}\exp\Bigl(\frac{2\pi i}{q}(ac+bd)\Bigr)\right|^2 .Pr[c,d,gk]=​(p−1)q1​0≤a,b≤p−2a−rb≡k (p−1)​∑​exp(q2πi​(ac+bd))​2.
  1. Each good state is likely, eq. (6.17). If (c,d)(c,d)(c,d) is good, then Pr⁡[c,d,y]≥1/(20q2)\Pr[c,d,y]\ge 1/(20q^2)Pr[c,d,y]≥1/(20q2) for every yyy.
  2. Many good pairs (p. 1504). At least q/12q/12q/12 pairs (c,d)(c,d)(c,d) are good.
  3. Each good ccc is likely (p. 1504). If (c,d)(c,d)(c,d) is good for some ddd, then ∑d′,yPr⁡[c,d′,y]≥(p−1)/(20q2)≥1/(40q)\sum_{d',y}\Pr[c,d',y]\ge(p-1)/(20q^2)\ge1/(40q)∑d′,y​Pr[c,d′,y]≥(p−1)/(20q2)≥1/(40q).

Significance

The result. The bound 1/4801/4801/480 is what turns the circuit into an algorithm. Repeating the circuit O(1)O(1)O(1) times in expectation yields a good output, and from a good pair (c,d)(c,d)(c,d) one reads off an equation that determines rrr modulo divisors of p−1p-1p−1 (§6, eqs. (6.18)–(6.20)). Together with the quantum Fourier transform circuit and reversible modular exponentiation, this places the discrete logarithm modulo a prime in quantum polynomial time. Every later analysis of quantum attacks on discrete-logarithm cryptography starts from this success probability or a sharpened version of it.

Formalizing it. The result has been proved since 1994–1997 and is textbook material; it is not open. As far as is known, no machine-checked proof of Shor's discrete-logarithm analysis exists. The paper's proof of eq. (6.17) replaces a sum by an integral with an error term O(W/(pq))O(W/(pq))O(W/(pq)) whose constant is not given, yet states 1/(20q2)1/(20q^2)1/(20q2) for every prime. A formal proof must therefore either control that error explicitly or find another argument, and so settles a point the paper leaves informal. Numerically, the smallest value of q2Pr⁡[c,d,y]q^2\Pr[c,d,y]q2Pr[c,d,y] over good states is about 0.490.490.49 for all primes p<90p<90p<90, so the unconditional claim is not in doubt for small ppp. The page also contains two small slips, recorded under Formalization scope; a complete development pins down exactly what is true.

Difficulty

The exponential sum (6.4) runs over pairs (a,b)(a,b)(a,b) satisfying a congruence modulo p−1p-1p−1, while the phases are taken modulo qqq. The two moduli are unrelated: qqq is a power of two and p−1p-1p−1 is arbitrary. Eliminating aaa through the congruence introduces a floor function ⌊(br+k)/(p−1)⌋\lfloor(br+k)/(p-1)\rfloor⌊(br+k)/(p−1)⌋, and the resulting phase is not linear in bbb. The obvious estimate treats the sum as a geometric series in bbb and bounds it by its first-order phase; this fails because the floor term perturbs every phase by an amount of size up to ∣{c(p−1)}q∣|\{c(p-1)\}_q|∣{c(p−1)}q​∣. Condition (6.11) only keeps this perturbation within π/6\pi/6π/6 of the main phase; it does not remove it. The per-state bound must survive this perturbation uniformly in ppp, rrr and kkk, including small primes where the paper's integral approximation gives no explicit control.

The count of good pairs needs a separate argument about how often a multiple c(p−1)c(p-1)c(p−1) lies within q/12q/12q/12 of a multiple of qqq when gcd⁡(p−1,q)\gcd(p-1,q)gcd(p−1,q) is large.

Formalization scope

  • States are functions Fin q × Fin q × (ZMod p)ˣ → ℂ. The first two registers range over {0,…,q−1}\{0,\dots,q-1\}{0,…,q−1}; the third over the units modulo ppp.
  • Matrix convention. Following §2, rows are inputs, so the amplitude of ∣c,d,y⟩|c,d,y\rangle∣c,d,y⟩ after the transforms is ∑a,bψ(a,b,y)(Aq)a,c(Aq)b,d\sum_{a,b}\psi(a,b,y)(A_q)_{a,c}(A_q)_{b,d}∑a,b​ψ(a,b,y)(Aq​)a,c​(Aq​)b,d​. finalState is defined this way from (6.1) and AqA_qAq​. It is not typed in as the closed form (6.3) or (6.4). A formalization that defined the final state by (6.4) directly would make milestone 1 trivial, and is ruled out.
  • Probability of a basis state is the squared norm of its amplitude, with no normalization hypothesis.
  • Parameters. ppp is prime (Fact p.Prime). The generator is encoded as orderOf g = p - 1. r<p−1r<p-1r<p−1 is a parameter, with x=grx=g^rx=gr. qqq is given by q = 2 ^ l together with p<q<2pp<q<2pp<q<2p. No large-ppp threshold is added anywhere.
  • Arithmetic. x−bx^{-b}x−b is x⁻¹ ^ b in the unit group. p−1p-1p−1 is computed in Z\mathbb ZZ and R\mathbb RR inside TTT and the congruences, and as natural-number subtraction only where p≥2p\ge2p≥2 makes it exact. TTT is real.
  • Condition (6.10) is stated as "some integer jjj has ∣T−jq∣≤12|T-jq|\le\frac12∣T−jq∣≤21​". Because q≥4q\ge4q≥4, this is equivalent to the page's form with jjj the closest integer to T/qT/qT/q.
  • Not formalized. The preparation of (6.1) by testing and restarting is not formalized; the state (6.1) is taken as displayed. The printed test "whether the number is less than ppp" should read p−1p-1p−1, as the sums in (6.1) show. Also out of scope: the recovery of rrr (eqs. (6.18)–(6.20)), the repetition count "480t480t480t", and all running-time claims.
  • Printed slips.
    • The page asserts that for each ccc there is exactly one ddd satisfying (6.10). At a tie {T}q=±12\{T\}_q=\pm\frac12{T}q​=±21​ there can be two such ddd. Milestone 3 states only the count, which needs at least one.
    • The page's intermediate bound "at least p/(240q)p/(240q)p/(240q)" should be (p−1)/(240q)(p-1)/(240q)(p−1)/(240q). The conclusion 1/4801/4801/480 is unaffected, since qqq and 2p2p2p are both even and so q≤2(p−1)q\le 2(p-1)q≤2(p−1). Only 1/4801/4801/480 is stated.

Needed infrastructure: finite exponential sums and their modulus, the symmetric residue and its basic properties, and counting multiples in residue classes of Z/q\mathbb Z/qZ/q. The exponential-sum estimates of milestones 1 and 2 are reusable in the order-finding analysis of §5 of the same paper. Proofs of any milestone, of the normalization ∑Pr⁡=1\sum\Pr=1∑Pr=1, and of auxiliary lemmas about symmRes are welcome.

Selected references

  • P. W. Shor, Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer, SIAM J. Comput. 26(5):1484–1509, 1997. https://doi.org/10.1137/S0097539795293172 (preprint: https://arxiv.org/abs/quant-ph/9508027)
  • D. M. Gordon, Discrete logarithms in GF(p) using the number field sieve, SIAM J. Discrete Math. 6(1):124–138, 1993. https://doi.org/10.1137/0406010
  • W. Diffie and M. E. Hellman, New directions in cryptography, IEEE Trans. Inform. Theory 22(6):644–654, 1976. https://doi.org/10.1109/TIT.1976.1055638
  • M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information, Cambridge University Press, 2000. https://doi.org/10.1017/CBO9780511976667
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Harmonic AnalysisTheoretical Computer Science·Captain: mikedeng1

Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer 1: The Quantum Fourier Transform Circuit Computes A_q up to Bit ReversalResearch Paper

Motivation

Shor's factoring and discrete logarithm algorithms (Shor 1997) reduce both problems to sampling from the output of a quantum Fourier transform: a register holding a superposition with a hidden period is transformed, then measured, and the measured value carries information about the period. The whole speed-up rests on one engineering fact: for q=2lq = 2^lq=2l the q×qq \times qq×q Fourier matrix, which has q2=4lq^2 = 4^lq2=4l entries, can be applied by a quantum circuit of only O(l2)O(l^2)O(l2) elementary gates, each acting on one or two bits.

That circuit was found independently by Coppersmith (IBM RC 19642, 1994) and Deutsch, and Shor's §4 presents it following Ekert and Jozsa (Rev. Mod. Phys. 68, 1996). It is the quantum analogue of the radix-2 fast Fourier transform, and it reappears in phase estimation, in the hidden subgroup algorithms for abelian groups, and in every textbook account of quantum computation. This mission formalizes Shor's statement that the circuit computes the Fourier matrix, up to a reversal of the output bits, together with the two displayed steps of its verification.

Setting

A register of lll bits has one basis state ∣a⟩=∣al−1al−2…a0⟩|a\rangle = |a_{l-1} a_{l-2} \dots a_0\rangle∣a⟩=∣al−1​al−2​…a0​⟩ for every bit string, with a0a_0a0​ the least significant bit; the string encodes the integer

a=∑j=0l−12jaj,0≤a<q=2l.a = \sum_{j=0}^{l-1} 2^j a_j, \qquad 0 \le a < q = 2^l .a=j=0∑l−1​2jaj​,0≤a<q=2l.

A state is a vector of complex amplitudes, one per basis state. A gate is a matrix whose rows are indexed by input basis vectors and whose columns are indexed by output basis vectors (§2, p. 1489). A gate on some of the bits acts on those bits through its matrix and leaves the others alone (§2, p. 1490): the output amplitude at a string bbb is the sum, over the possible input values uuu of the acted-on bits, of the input amplitude at bbb with those bits replaced by uuu, times the matrix entry from uuu to the corresponding bits of bbb.

The Fourier matrix AqA_qAq​ (eq. (4.1)) is the q×qq \times qq×q matrix with (a,c)(a, c)(a,c) entry q−1/2exp⁡(2πi ac/q)q^{-1/2}\exp(2\pi i\,ac/q)q−1/2exp(2πiac/q); it takes ∣a⟩|a\rangle∣a⟩ to q−1/2∑c=0q−1exp⁡(2πi ac/q) ∣c⟩q^{-1/2}\sum_{c=0}^{q-1}\exp(2\pi i\,ac/q)\,|c\rangleq−1/2∑c=0q−1​exp(2πiac/q)∣c⟩.

The circuit uses two gates (eqs. (4.2), (4.3)):

  • RjR_jRj​ acts on bit jjj with matrix 12(111−1)\frac{1}{\sqrt 2}\begin{pmatrix} 1 & 1 \\ 1 & -1\end{pmatrix}2​1​(11​1−1​);
  • Sj,kS_{j,k}Sj,k​, for j<kj < kj<k, acts on bits jjj and kkk with matrix diag(1,1,1,eiθk−j)\mathrm{diag}(1, 1, 1, e^{i\theta_{k-j}})diag(1,1,1,eiθk−j​), where θk−j=π/2k−j\theta_{k-j} = \pi / 2^{k-j}θk−j​=π/2k−j; it multiplies the amplitude of a basis state by eiθk−je^{i\theta_{k-j}}eiθk−j​ when bits jjj and kkk are both 111.

The circuit is the gate sequence (4.4), applied from left to right:

Rl−1 Sl−2,l−1 Rl−2 Sl−3,l−1 Sl−3,l−2 Rl−3⋯R1 S0,l−1 S0,l−2⋯S0,2 S0,1 R0,R_{l-1}\, S_{l-2,l-1}\, R_{l-2}\, S_{l-3,l-1}\, S_{l-3,l-2}\, R_{l-3} \cdots R_1\, S_{0,l-1}\, S_{0,l-2} \cdots S_{0,2}\, S_{0,1}\, R_0 ,Rl−1​Sl−2,l−1​Rl−2​Sl−3,l−1​Sl−3,l−2​Rl−3​⋯R1​S0,l−1​S0,l−2​⋯S0,2​S0,1​R0​,

that is, for j=l−1,…,0j = l-1, \dots, 0j=l−1,…,0 it applies Sj,l−1,…,Sj,j+1S_{j,l-1}, \dots, S_{j,j+1}Sj,l−1​,…,Sj,j+1​ and then RjR_jRj​. On three bits it is R2S1,2R1S0,2S0,1R0R_2 S_{1,2} R_1 S_{0,2} S_{0,1} R_0R2​S1,2​R1​S0,2​S0,1​R0​.

The bit reversal of a string bbb is the string ccc with ck=bl−1−kc_k = b_{l-1-k}ck​=bl−1−k​.

Formalization targets

Goal: the circuit computes AqA_qAq​ up to bit reversal (§4, p. 1496)

For every l≥0l \ge 0l≥0 and every basis state ∣a⟩|a\rangle∣a⟩, with q=2lq = 2^lq=2l,

circuit ∣a⟩=1q1/2∑bexp⁡(2πi ac/q) ∣b⟩,c=bit reversal of b.\text{circuit}\,|a\rangle = \frac{1}{q^{1/2}}\sum_{b}\exp(2\pi i\,ac/q)\,|b\rangle, \qquad c = \text{bit reversal of } b .circuit∣a⟩=q1/21​b∑​exp(2πiac/q)∣b⟩,c=bit reversal of b.

The sum runs over all lll-bit strings bbb. Reading the output register in reverse order therefore yields Aq∣a⟩A_q|a\rangleAq​∣a⟩.

Milestone 1: the amplitude along the circuit (§4, eq. (4.5), p. 1496)

The amplitude of ∣b⟩|b\rangle∣b⟩ in circuit ∣a⟩\text{circuit}\,|a\ranglecircuit∣a⟩ is

2−l/2exp⁡(i(∑0≤j<lπajbj+∑0≤j<k<lπ2k−jajbk)).2^{-l/2}\exp\Big(i\Big(\sum_{0\le j<l}\pi a_jb_j + \sum_{0\le j<k<l}\frac{\pi}{2^{k-j}}a_jb_k\Big)\Big).2−l/2exp(i(0≤j<l∑​πaj​bj​+0≤j<k<l∑​2k−jπ​aj​bk​)).

Milestone 2: the phase identity (§4, eqs. (4.6)–(4.10), pp. 1496–1497)

For bit strings a,ba, ba,b, with ccc the bit reversal of bbb and a,ca, ca,c their values,

exp⁡(i(∑0≤j<lπajbj+∑0≤j<k<lπ2k−jajbk))=exp⁡(2πi ac/q).\exp\Big(i\Big(\sum_{0\le j<l}\pi a_jb_j + \sum_{0\le j<k<l}\frac{\pi}{2^{k-j}}a_jb_k\Big)\Big) = \exp(2\pi i\,ac/q).exp(i(0≤j<l∑​πaj​bj​+0≤j<k<l∑​2k−jπ​aj​bk​))=exp(2πiac/q).

Significance

The result. The goal says that AqA_qAq​, a dense unitary on 2l2^l2l amplitudes, is realized by lll one-bit gates and l(l−1)/2l(l-1)/2l(l−1)/2 two-bit gates, followed by a relabelling of the output. This is what makes the Fourier sampling step of the factoring algorithm (§5) and of the discrete logarithm algorithm (§6) polynomial in the number of bits. Without it, the analyses of those sections describe measurements of states that no efficient circuit is known to prepare. The bit reversal is a real part of the statement: the circuit does not compute AqA_qAq​ itself for l≥2l \ge 2l≥2, and an implementation must either permute the output bits or read them in reverse order.

Formalizing it. The identity is classical and its proof is short on paper; its content lies in bookkeeping that is easy to get wrong: which bit a gate touches, which of the input or output value a bit holds when a phase gate acts, and the order in which the gates are applied. A machine-checked version fixes all of these conventions explicitly and yields a reusable model of gate-level circuits on bit strings. To the drafter's knowledge there is no Lean formalization of this circuit on Prove2Me; the platform's FastFourierTransform rows state the classical Cooley–Tukey recursion for the unnormalized transform with the opposite sign, which is a different object.

Difficulty

Multiplying out the gate matrices is not an argument beyond tiny lll: the difficulty is to control the whole product of l(l+1)/2l(l+1)/2l(l+1)/2 gates symbolically. The paper's argument (§4, p. 1496) is informal at exactly the points a formal proof must make precise: that only one sequence of intermediate basis states from ∣a⟩|a\rangle∣a⟩ to ∣b⟩|b\rangle∣b⟩ carries nonzero amplitude, and that at the moment Sj,kS_{j,k}Sj,k​ acts, bit kkk already holds its output value bkb_kbk​ while bit jjj still holds its input value aja_jaj​. Both facts depend on the order (4.4); applying the same gates in the reverse order gives a different unitary, whose phase pairs bjb_jbj​ with aka_kak​. The phase identity then holds only modulo 2π2\pi2π, not as an equality of real numbers, so it cannot be closed by rearranging sums alone.

Formalization scope

  • States. A bit string on lll bits is Fin l → Fin 2, with bit j equal to aja_jaj​ and value ∑j2jaj\sum_j 2^j a_j∑j​2jaj​ (least significant bit first). A state is a function from bit strings to ℂ. The case l=0l = 0l=0 (q=1q = 1q=1, empty circuit) is included.
  • Gate application follows the row = input convention: a one-bit gate MMM on bit jjj sends ψ\psiψ to b↦∑uψ(b[j↦u]) Mu,bjb \mapsto \sum_u \psi(b[j\mapsto u])\,M_{u, b_j}b↦∑u​ψ(b[j↦u])Mu,bj​​, and a two-bit gate acts analogously on the pair (bit jjj, bit kkk).
  • The circuit is the gate list (4.4) — an explicit list of constructors R j and S j k — run by a left fold, so the leftmost gate acts first. It is not defined as the matrix AqA_qAq​ or by its entries; a definition of that kind would make the goal true by unfolding and is ruled out.
  • Angles. θk−j=π/2k−j\theta_{k-j} = \pi/2^{k-j}θk−j​=π/2k−j uses natural-number subtraction, which is exact because the list only contains Sj,kS_{j,k}Sj,k​ with j<kj < kj<k.
  • Normalization. The prefactor q−1/2q^{-1/2}q−1/2 is written (2l)−1(\sqrt{2^l})^{-1}(2l​)−1.
  • Basis form. The goal is stated on basis states, as on the page; by linearity it determines the circuit on every state.
  • Not stated. The gate count: the paper's sentence "we thus need to use l(l−1)/2l(l-1)/2l(l−1)/2 quantum gates" (p. 1496) counts only the gates Sj,kS_{j,k}Sj,k​; the sequence (4.4) also contains the lll gates RjR_jRj​, l(l+1)/2l(l+1)/2l(l+1)/2 gates in all. The approximate transform of Coppersmith and the one-bit construction of Griffiths and Niu (p. 1497) are out of scope, as is the polynomial-time claim.

Welcome contributions: proofs of the two milestones and the goal; general lemmas about one- and two-bit gate actions on Fin l → Fin 2 states (commutation of gates on disjoint bits, linearity, action on basis states), which are reusable for any gate-level circuit; and a proof that the circuit is unitary.

Selected references

  • P. W. Shor, Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer, SIAM J. Comput. 26(5):1484–1509, 1997. https://doi.org/10.1137/S0097539795293172
  • D. Coppersmith, An approximate Fourier transform useful in quantum factoring, IBM Research Report RC 19642, 1994. https://arxiv.org/abs/quant-ph/0201067
  • A. Ekert and R. Jozsa, Quantum computation and Shor's factoring algorithm, Rev. Mod. Phys. 68:733–753, 1996. https://doi.org/10.1103/RevModPhys.68.733
  • R. B. Griffiths and C.-S. Niu, Semiclassical Fourier transform for quantum computation, Phys. Rev. Lett. 76:3228–3231, 1996. https://doi.org/10.1103/PhysRevLett.76.3228
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Quantum Error Correction·Captain: Lucas

Knill-Laflamme-Zurek: Resilient Quantum Computation and the Error ThresholdResearch Paper

Motivation

A quantum computer manipulates superpositions of many degrees of freedom, and every gate it applies is imperfect. Before 1996 it was widely argued that this fragility is fatal: the accuracy demanded of each elementary operation appeared to grow with the length of the computation, so that arbitrarily long computations would require arbitrarily perfect hardware. The discovery of quantum error-correcting codes, of fault tolerant syndrome extraction, and of transversally encoded operations changed that picture, but the first fault tolerant schemes still required the error per operation to shrink as the computation grew.

Knill, Laflamme and Zurek, Resilient Quantum Computation: Error Models and Thresholds (arXiv:quant-ph/9702058), together with the independent work of Kitaev and of Aharonov and Ben-Or, removed that requirement: they exhibited a fixed threshold value such that any physical error rate below it permits arbitrarily accurate encoded computation, at an overhead that is only polylogarithmic in the length of the computation. Their paper is also the one that pushes past independent stochastic noise: its thresholds are proved for quasi-independent error models, which accommodate coherent over-rotation and weak residual couplings between neighbouring qubits.

This mission formalizes the quantitative engine of that paper: the concatenation recursion, the error-model bounds it is applied to, the threshold it produces, and the resulting overhead.

Setting

Fix a physical implementation in which each gate, state preparation and idle memory step carries an error location. A noisy network with error locations indexed by a finite set locs\mathrm{locs}locs is described through an error expansion; each summand fails at a set of locations, and the model constrains how probable simultaneous failures are.

Write μ\muμ for the underlying probability measure and FiF_iFi​ for the event that location iii has failed. Two models from Section I.B of the paper are used here.

  • Quasi-independent stochastic model with error probability ppp: for every set S⊆locsS \subseteq \mathrm{locs}S⊆locs of error locations,
μ(⋂i∈SFi)≤p∣S∣.\mu\Big(\bigcap_{i \in S} F_i\Big) \le p^{|S|}.μ(i∈S⋂​Fi​)≤p∣S∣.
  • Quasi-independent monotonic model with constant CCC and parameter ppp: the same quantity is bounded by C p∣S∣C\,p^{|S|}Cp∣S∣.

The fault tolerant construction of Section I turns physical qubits into encoded qubits: each encoded gate is a transversal operation preceded by fault tolerant recovery networks built from the 777-qubit Steane code. The analysis of Section II declares an encoded gate failed according to an explicit combinatorial rule, and shows that a failure requires two failures among a set of fff minimal pairs of physical error locations. Consequently one level of encoding maps a failure parameter ppp to fp2f p^{2}fp2.

Iterating this reduction is concatenation. Define the level-hhh failure parameter by

E0=p,Eh+1=f Eh2,E_0 = p, \qquad E_{h+1} = f\,E_h^{2},E0​=p,Eh+1​=fEh2​,

written Eh=levelError f p hE_h = \texttt{levelError}\,f\,p\,hEh​=levelErrorfph in the Lean development. Let KKK bound the number of qubits and time steps at the level below that are consumed by one encoded gate, so that one computational gate at level hhh costs KhK^{h}Kh physical resources.

Formalization targets

Goal — threshold theorem with polylogarithmic overhead

For f≥1f \ge 1f≥1, K≥2K \ge 2K≥2, 0≤p<1/f0 \le p < 1/f0≤p<1/f, and every gate count nnn and target failure probability qqq with 0<q<n0 < q < n0<q<n, there exists a level hhh with

n Eh<qandKh  ≤  K2(max⁡(1, log⁡(n/q)log⁡(1/(fp))))log⁡2K.n\,E_h < q \qquad\text{and}\qquad K^{h} \;\le\; K^{2}\Big(\max\Big(1,\ \frac{\log(n/q)}{\log\big(1/(fp)\big)}\Big)\Big)^{\log_2 K}.nEh​<qandKh≤K2(max(1, log(1/(fp))log(n/q)​))log2​K.

The first clause is resilience: a computation of nnn encoded gates can be made to fail with probability below any prescribed qqq. The second is the cost: the overhead per computational gate is polylogarithmic in n/qn/qn/q, with exponent log⁡2K\log_2 Klog2​K. The threshold itself is the hypothesis p<1/fp < 1/fp<1/f; no numerical value is hard-coded, so the statement survives any improvement of the paper's count f≤337195f \le 337195f≤337195 and of the resulting bounds 3.0×10−63.0 \times 10^{-6}3.0×10−6 and 1.3×10−61.3 \times 10^{-6}1.3×10−6.

Supporting targets

The milestone list covers the closed form Eh=f2h−1p2hE_h = f^{2^{h}-1} p^{2^{h}}Eh​=f2h−1p2h of the concatenation recursion and its threshold form Eh=(fp)2h/fE_h = (fp)^{2^{h}}/fEh​=(fp)2h/f; convergence Eh→0E_h \to 0Eh​→0 below threshold; the failure bounds ∣locs∣ p|\mathrm{locs}|\,p∣locs∣p and C ∣locs∣ pC\,|\mathrm{locs}|\,pC∣locs∣p for a network under the two error models; the criterion 2h>log⁡(n/q)/log⁡(1/(fp))2^{h} > \log(n/q)/\log(1/(fp))2h>log(n/q)/log(1/(fp)) under which n(fp)2h<qn (fp)^{2^{h}} < qn(fp)2h<q; and the overhead estimate K⌈log⁡2L⌉≤KLlog⁡2KK^{\lceil \log_2 L\rceil} \le K L^{\log_2 K}K⌈log2​L⌉≤KLlog2​K.

Significance

The threshold theorem is the reason large-scale quantum computation is regarded as a physically meaningful goal rather than an idealization: it converts an engineering requirement that scales with the problem size into a fixed, size-independent accuracy target, and it fixes the currency — threshold value and overhead exponent — in which every later architecture is compared. The quasi-independent models matter separately: they show the conclusion does not depend on noise being stochastic, which is what rules out the objection that coherent over-rotations invalidate the stochastic analysis.

The paper's own argument is a physics-style derivation: a circuit-level construction, a combinatorial count of failure-inducing pairs of error locations, and a recursion. This mission formalizes the recursion, the probabilistic bounds and the resulting threshold and overhead statements. It does not formalize the circuit-level content — the 777-qubit code, the cat-state syndrome extraction networks, transversality, or the count f≤337195f \le 337195f≤337195 itself; these enter the formal statements as the parameters fff and KKK. No machine-checked proof of the full threshold theorem, in the sense of a verified fault tolerant circuit construction, is known to the author of this proposal; formalizing the quantitative layer is a prerequisite for one.

Difficulty

The obvious route to "errors vanish under concatenation" is to iterate the bound p↦fp2p \mapsto f p^{2}p↦fp2 and observe the doubly exponential decay. That step is the easy half, and the milestone list makes it explicit. What the naive argument silently assumes is exactly what Section II must establish: that the level-(h+1)(h+1)(h+1) failure events again obey a quasi-independent bound, with kkk simultaneous encoded failures bounded by (fp2)k(f p^{2})^{k}(fp2)k rather than merely each single failure bounded by fp2f p^{2}fp2. This is why the paper's failure declaration is defined backwards in time and demands disjoint pairs of contributing locations. A formalization that bounds only single-gate failure probabilities cannot close the induction.

The second difficulty is bookkeeping at the edges: the recursion is stated for a failure parameter that is a probability in one model and an operator strength in the other, and the overhead bound involves a real exponent log⁡2K\log_2 Klog2​K and a ceiling, whose interaction with the degenerate cases p=0p = 0p=0 and n≤qn \le qn≤q has to be handled rather than assumed away.

Formalization scope

All parameters are real numbers: f,p,q,K,C∈Rf, p, q, K, C \in \mathbb{R}f,p,q,K,C∈R, and the gate count nnn is a natural number cast to R\mathbb{R}R. levelError is defined by recursion on the level and is total, so it is meaningful for every real f,pf, pf,p; the threshold hypothesis appears explicitly as p<1/fp < 1/fp<1/f where it is needed.

The two error models are predicates on a measure μ\muμ on a measurable space, a Finset of error locations, and a family of failure events, with values in ENNReal; they quantify over all subsets SSS of the location set, matching the paper's "at a given kkk many error locations". The network failure bounds are stated for μ(⋃iFi)\mu\big(\bigcup_{i} F_i\big)μ(⋃i​Fi​), the probability that some location fails.

The goal is not vacuous: the hypotheses f≥1f \ge 1f≥1, K≥2K \ge 2K≥2, 0≤p<1/f0 \le p < 1/f0≤p<1/f, 0<q<n0 < q < n0<q<n are simultaneously satisfiable (for instance f=1f = 1f=1, K=2K = 2K=2, p=10−6p = 10^{-6}p=10−6, n=106n = 10^{6}n=106, q=1/2q = 1/2q=1/2), and the conclusion asserts a strict inequality together with a quantitative overhead bound, so it cannot be discharged by a degenerate reading. Degenerate inputs are inside the statement rather than excluded: p=0p = 0p=0, and the case log⁡(n/q)/log⁡(1/(fp))≤1\log(n/q)/\log(1/(fp)) \le 1log(n/q)/log(1/(fp))≤1, are covered by the max⁡\maxmax and must be handled.

A complete development needs only Mathlib's real analysis (Real.log, Real.logb, Real.rpow, Nat.ceil) and the measure-theoretic union bound; nothing quantum is required by the formal statements. Contributions strengthening the milestone list towards the circuit level — a formal model of error locations in a network, of the induced next-level error model, or of stabilizer-code recovery — are welcome as further targets.

Selected references

  • E. Knill, R. Laflamme, W. H. Zurek, Resilient Quantum Computation: Error Models and Thresholds, arXiv:quant-ph/9702058 (1997), https://arxiv.org/abs/quant-ph/9702058
  • A. M. Steane, Error Correcting Codes in Quantum Theory, Physical Review Letters 77, 793 (1996), https://doi.org/10.1103/PhysRevLett.77.793
  • P. W. Shor, Fault-tolerant quantum computation, Proceedings of the 37th Symposium on Foundations of Computer Science (1996), https://arxiv.org/abs/quant-ph/9605011
  • D. Aharonov, M. Ben-Or, Fault-Tolerant Quantum Computation With Constant Error, arXiv:quant-ph/9611025 (1996), https://arxiv.org/abs/quant-ph/9611025
  • A. Yu. Kitaev, Quantum computations: algorithms and error correction, Russian Mathematical Surveys 52, 1191 (1997), https://doi.org/10.1070/RM1997v052n06ABEH002155
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Quantum Error Correction·Captain: Lucas

Aharonov-Ben-Or Threshold: Recursive Sparseness Below the ThresholdResearch Paper

Motivation

Quantum computations are performed on physical devices whose elementary operations are never exact: every gate, every idle qubit, every time step is subject to decoherence, damping and systematic inaccuracy. Without protection these errors accumulate and destroy the computation. In 1997 Dorit Aharonov and Michael Ben-Or proved that this can be overcome: as long as the error rate — the probability, per location per time step, that a fault occurs — is below a fixed positive threshold, an arbitrary quantum circuit can be simulated reliably with only polylogarithmic overhead (arXiv:quant-ph/9906129). This threshold theorem is the reason large-scale quantum computing is regarded as physically possible in principle, and the number it produces is the target every hardware programme is measured against.

The engine of the proof is not quantum mechanical but combinatorial. A circuit is encoded, then the encoded circuit is encoded again, rrr times over. The locations of the resulting circuit are organised into a tree of nested rectangles, and the computation survives precisely when the set of faulty locations is sparse in a recursive sense. The mission formalizes that engine: the recursive sparseness calculus of §7.4–7.6 and §8.2 of the paper, which converts a constant error rate below the threshold into a doubly exponentially small failure probability.

Setting

Fix two natural numbers: AAA, the number of locations in a rectangle, and kkk, the number of faulty sub-rectangles a rectangle tolerates (in the paper k=⌊d/l⌋k=\lfloor d/l\rfloork=⌊d/l⌋, where ddd is the number of errors the code corrects and lll is the spread of a fault). A 000-rectangle is a single location of the circuit, and an (r+1)(r+1)(r+1)-rectangle consists of AAA many rrr-rectangles. A fault pattern of an rrr-rectangle records, for each of its ArA^{r}Ar locations, whether a fault occurred there.

Following Definition 18 of the paper, a fault pattern is (r,k)(r,k)(r,k)-sparse by recursion on rrr: at level 000 it is sparse when the location carries no fault; at level r+1r+1r+1 it is sparse when at most kkk of the AAA constituent rrr-rectangles carry a pattern that is not (r,k)(r,k)(r,k)-sparse. Under independent probabilistic noise of rate η\etaη each location is faulty with probability η\etaη, independently; write P(r)P(r)P(r) for the probability that the pattern of an rrr-rectangle is (r,k)(r,k)(r,k)-sparse, and 1−P(r)1-P(r)1−P(r) for the probability that it is not.

Two thresholds appear. The threshold condition for probabilistic noise (Definition 19) is

(Ak+1) ηk+1<η,\binom{A}{k+1}\,\eta^{k+1} < \eta ,(k+1A​)ηk+1<η,

and the threshold condition for general noise (Definition 21) is

e(Ak+1)(2η)k+1<2η.e\binom{A}{k+1}(2\eta)^{k+1} < 2\eta .e(k+1A​)(2η)k+1<2η.

The corresponding thresholds are ηc=(Ak+1)−1/k\eta_c=\binom{A}{k+1}^{-1/k}ηc​=(k+1A​)−1/k and ηc′=12(e(Ak+1))−1/k\eta_c'=\tfrac12\bigl(e\binom{A}{k+1}\bigr)^{-1/k}ηc′​=21​(e(k+1A​))−1/k: every positive η\etaη below them satisfies the respective condition.

Target

The goal is the quantitative core of Theorem 10. Below the threshold there are constants C,c>0C,c>0C,c>0 such that for every circuit size v≥1v\ge 1v≥1 and every accuracy 0<ε<10<\varepsilon<10<ε<1 some recursion depth rrr satisfies both

v⋅(1−P(r))<εandAr≤C(1+log⁡vε)c.v\cdot\bigl(1-P(r)\bigr) < \varepsilon \qquad\text{and}\qquad A^{r} \le C\Bigl(1+\log\frac{v}{\varepsilon}\Bigr)^{c}.v⋅(1−P(r))<εandAr≤C(1+logεv​)c.

The first inequality says that, over all vvv rectangles of the simulated circuit, the probability that any of them carries a non-sparse fault pattern is below ε\varepsilonε; the second says that the overhead per simulated location, ArA^{r}Ar, is polylogarithmic in v/εv/\varepsilonv/ε — the cost claimed in Theorem 10.

The milestones build up to it: the two threshold conditions hold below the respective thresholds, the exponent gap δ\deltaδ of equation 7.5 exists, the base case P(0)=1−ηP(0)=1-\etaP(0)=1−η, the union-bound recursion 1−P(r+1)≤(Ak+1)(1−P(r))k+11-P(r+1)\le\binom{A}{k+1}(1-P(r))^{k+1}1−P(r+1)≤(k+1A​)(1−P(r))k+1, Lemma 10 itself (P(r)≥1−η(1+δ)rP(r)\ge 1-\eta^{(1+\delta)^r}P(r)≥1−η(1+δ)r), and the analogous recursion 8.8–8.10 governing the bad part in the general-noise analysis.

Significance

Lemma 10 is where a constant error rate becomes a vanishing one. Each level of concatenation only has to improve reliability slightly, because the improvement compounds doubly exponentially in the number of levels; this is exactly why constant-rate noise can be tolerated while Shor's earlier scheme needed a polylogarithmically small rate. The same recursion, with 2η2\eta2η in place of η\etaη and an extra factor of eee, controls the trace norm of the bad part of the fault-path expansion in the general-noise model (§8.2), which is how the result is extended from probabilistic errors to decoherence, damping and systematic inaccuracy.

Formalizing this part of the paper yields a reusable, model-independent statement: nothing in the milestones mentions density matrices, so the sparseness calculus can later be plugged into a formal model of noisy quantum circuits, or reused for classical concatenated fault tolerance. What this mission does not do is formalize the circuit-level statement of Theorem 10 — that requires a full Lean model of quantum circuits with mixed states, fault-tolerant procedures, and the code constructions of §3–§6, and is deliberately out of scope here.

Difficulty

The obvious approach — bound the failure probability by a union bound over all fault sets of size k+1k+1k+1 and iterate — is exactly right, but the iteration is where the work is. The recursion qr+1≤(Ak+1)qrk+1q_{r+1}\le\binom{A}{k+1}q_r^{k+1}qr+1​≤(k+1A​)qrk+1​ only produces the doubly exponential decay η(1+δ)r\eta^{(1+\delta)^r}η(1+δ)r once the threshold condition is converted into the strict exponent gap δ\deltaδ of equation 7.5, and the induction has to keep qr≤ηq_r\le\etaqr​≤η alive to reuse that gap at every level. In the general-noise recursion there is a second trap: the factor (1+br)A−k−1(1+b_r)^{A-k-1}(1+br​)A−k−1 exceeds 111, so the recursion is not contracting termwise, and one needs 2ηA≤12\eta A\le 12ηA≤1 to bound it by eee.

On the probabilistic side, the statements are about a genuine product distribution on an exponentially large sample space, not a recursively defined real number: the union-bound step must be carried out over the actual set of fault patterns, where independence across the AAA sub-rectangles is what makes the product bound valid.

Formalization scope

Fault patterns are a type defined by recursion on the level: FaultPattern(A,0)\mathrm{FaultPattern}(A,0)FaultPattern(A,0) is the booleans and FaultPattern(A,r+1)\mathrm{FaultPattern}(A,r+1)FaultPattern(A,r+1) is the functions from the AAA sub-rectangles to FaultPattern(A,r)\mathrm{FaultPattern}(A,r)FaultPattern(A,r); every level is a finite type with decidable equality. Sparseness is a decidable predicate following Definition 18 verbatim. The noise distribution is given by the weight of a pattern, the product over all locations of η\etaη (faulty) or 1−η1-\eta1−η (clean), and the sparseness probability is the sum of these weights over the sparse patterns — an honest finite product measure, so no hypothesis 0≤η≤10\le\eta\le10≤η≤1 is built into the definitions and each statement carries the range hypotheses it needs.

Two conventions are worth flagging. First, the thresholds are formalized as (Ak+1)−1/k\binom{A}{k+1}^{-1/k}(k+1A​)−1/k and 12(e(Ak+1))−1/k\tfrac12(e\binom{A}{k+1})^{-1/k}21​(e(k+1A​))−1/k; the paper prints the exponent as −k-k−k in equations 7.4 and 8.6, which is inconsistent with its own threshold conditions 7.3 and 8.5, and −1/k-1/k−1/k is the exponent for which the paper's claim "any η<ηc\eta<\eta_cη<ηc​ satisfies the threshold condition" is true. Second, Lemma 10 is stated with ≥\ge≥ rather than the paper's strict >>>, because at r=0r=0r=0 the two sides are equal.

Nothing here is vacuous: all hypotheses are satisfiable — for instance A=7A=7A=7, k=1k=1k=1, η=10−3\eta=10^{-3}η=10−3 satisfies every hypothesis of the goal — and the goal quantifies over all circuit sizes and all accuracies, so it cannot be discharged by a degenerate choice. A complete development needs only real analysis, finite sums and binomial counting from Mathlib. Contributions that generalise the branching factor to a per-rectangle bound Ai≤AA_i\le AAi​≤A, or that connect the calculus to a formal model of noisy circuits, are welcome.

Selected references

  • Dorit Aharonov and Michael Ben-Or, Fault-Tolerant Quantum Computation With Constant Error Rate, arXiv:quant-ph/9906129 (1999); preliminary version in Proc. 29th ACM STOC (1997). https://arxiv.org/abs/quant-ph/9906129
  • Peter W. Shor, Fault-tolerant quantum computation, Proc. 37th FOCS (1996). https://arxiv.org/abs/quant-ph/9605011
  • Dorit Aharonov, Alexei Kitaev and Noam Nisan, Quantum circuits with mixed states, Proc. 30th ACM STOC (1998). https://arxiv.org/abs/quant-ph/9806029
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Algebraic TopologyQuantum Error Correction·Captain: Rui Chao

Counterexamples to the Zeng–Pryadko Homological-Distance ConjectureResearch Paper

Background and main question

The tensor product of chain complexes is a basic construction in homological algebra and in the theory of quantum CSS codes. If AAA and BBB are finite based chain complexes over a finite field, their tensor product is graded by total degree,

(A⊗B)j=⨁i=0jAi⊗Bj−i.(A\otimes B)_j=\bigoplus_{i=0}^{j} A_i\otimes B_{j-i}.(A⊗B)j​=i=0⨁j​Ai​⊗Bj−i​.

Each complex carries a basis-dependent homological distance: dj(A)d_j(A)dj​(A) is the least Hamming weight of a degree-jjj cycle that is not a boundary, with dj(A)=∞d_j(A)=\inftydj​(A)=∞ when the degree-jjj homology vanishes. A natural candidate for the distance of the tensor product is therefore

mj(A,B)=min⁡0≤i≤jdi(A)dj−i(B).m_j(A,B)=\min_{0\le i\le j} d_i(A)d_{j-i}(B).mj​(A,B)=0≤i≤jmin​di​(A)dj−i​(B).

Every nonzero pair of homology classes in complementary degrees gives a pure-tensor class of the corresponding product weight, so one always has dj(A⊗B)≤mj(A,B)d_j(A\otimes B)\le m_j(A,B)dj​(A⊗B)≤mj​(A,B). The substantive question is whether this upper bound is always sharp.

In the preprint Higher-Dimensional Quantum Hypergraph-Product Codes with Finite Rates, posted in 2018 and subsequently published in Physical Review Letters, Zeng and Pryadko proved that the bound is sharp when one factor is a binary one-complex. Their exact formula is Eq. (13) of the arXiv version and is the capstone theorem of the Prove2Me mission Higher-Dimensional Quantum Hypergraph-Product Codes with Finite Rates. The present formalization is a direct sequel: it retains the same definitions and degree conventions while examining what happens when the one-complex restriction is removed.

Zeng and Pryadko later considered arbitrary finite chain complexes over arbitrary finite fields in Minimal distances for certain quantum product codes and tensor products of chain complexes, published in 2020. In the corresponding arXiv preprint, Conjecture 18 asserts the unrestricted equality

dj(A⊗B)=mj(A,B).d_j(A\otimes B)=m_j(A,B).dj​(A⊗B)=mj​(A,B).

The conjecture is appealing because it would make tensor-product distance completely compositional: the degreewise distances of the two factors would determine the distance of their product. The obstruction is that a homology class in A⊗BA\otimes BA⊗B need not have a minimum-weight representative supported in a single bidegree. A representative spread across several summands of the total complex can be lighter than every pure-tensor representative. The purpose of this formalization is to turn that observation into a concrete, machine-checked counterexample to Conjecture 18 while preserving the valid one-complex theorem as a sharply delimited special case.

The counterexample mechanism

The common foundation is recorded in the Prove2Me entry Based binary chain complexes and homological distance. In particular, the boundary in degree jjj is a map ∂j:Aj→Aj−1\partial_j:A_j\to A_{j-1}∂j​:Aj​→Aj−1​, and

dj(A)=inf⁡{wt⁡(x):x∈ker⁡∂j, x∉im⁡∂j+1}.d_j(A)=\inf\{\operatorname{wt}(x):x\in\ker\partial_j,\ x\notin\operatorname{im}\partial_{j+1}\}.dj​(A)=inf{wt(x):x∈ker∂j​, x∈/im∂j+1​}.

The one-complex result is separately available as Eq. (13) — Exact distance with a one-complex. The construction below uses exactly the same notion of distance, but both tensor factors are genuine three-term complexes.

Begin with binary CSS check maps

HX:F2n⟶F2rX,HZ:F2n⟶F2rZ,H_X:\mathbb F_2^n\longrightarrow\mathbb F_2^{r_X}, \qquad H_Z:\mathbb F_2^n\longrightarrow\mathbb F_2^{r_Z},HX​:F2n​⟶F2rX​​,HZ​:F2n​⟶F2rZ​​,

assumed surjective and satisfying HXHZT=HZHXT=0H_XH_Z^T=H_ZH_X^T=0HX​HZT​=HZ​HXT​=0. Suppose there are logical vectors x,z∈F2nx,z\in\mathbb F_2^nx,z∈F2n​ such that

HZx=0,HXz=0,x⋅z=1.H_Zx=0,\qquad H_Xz=0,\qquad x\cdot z=1.HZ​x=0,HX​z=0,x⋅z=1.

The check maps determine two dual three-term complexes

A:F2rX←HXF2n←HZTF2rZ,B:F2rZ←HZF2n←HXTF2rX.A:\quad \mathbb F_2^{r_X}\xleftarrow{H_X}\mathbb F_2^n \xleftarrow{H_Z^T}\mathbb F_2^{r_Z}, \qquad B:\quad \mathbb F_2^{r_Z}\xleftarrow{H_Z}\mathbb F_2^n \xleftarrow{H_X^T}\mathbb F_2^{r_X}.A:F2rX​​HX​​F2n​HZT​​F2rZ​​,B:F2rZ​​HZ​​F2n​HXT​​F2rX​​.

Their degree-two tensor space has three bidegree summands, corresponding to (2,0)(2,0)(2,0), (1,1)(1,1)(1,1), and (0,2)(0,2)(0,2). Under the natural matrix identifications, consider the element whose three blocks are

(IrZ,In,IrX).(I_{r_Z},I_n,I_{r_X}).(IrZ​​,In​,IrX​​).

The CSS orthogonality relations make this element a cycle. Its pairing with the chosen logical vectors certifies that it is not a boundary. Its Hamming weight is exactly rZ+n+rXr_Z+n+r_XrZ​+n+rX​, whereas the componentwise candidate in degree two reduces to

m2(A,B)=d1(A)d1(B).m_2(A,B)=d_1(A)d_1(B).m2​(A,B)=d1​(A)d1​(B).

Consequently, any CSS datum satisfying

rZ+n+rX<d1(A)d1(B)r_Z+n+r_X<d_1(A)d_1(B)rZ​+n+rX​<d1​(A)d1​(B)

produces the strict inequality d2(A⊗B)<m2(A,B)d_2(A\otimes B)<m_2(A,B)d2​(A⊗B)<m2​(A,B). For orientation, a binary quantum Golay CSS presentation with parameters [[23,1,7]][[23,1,7]][[23,1,7]] has rX=rZ=11r_X=r_Z=11rX​=rZ​=11, giving the numerical comparison 45<4945<4945<49. The formal proof must supply an explicit CSS instance and verify its algebraic and distance properties, rather than relying on the parameter notation alone.

Formalization objectives

The first milestone proves the general certificate: for every CSS datum satisfying the hypotheses above, the element (IrZ,In,IrX)(I_{r_Z},I_n,I_{r_X})(IrZ​​,In​,IrX​​) is a nontrivial degree-two cycle of weight rZ+n+rXr_Z+n+r_XrZ​+n+rX​, and the componentwise minimum is d1(A)d1(B)d_1(A)d_1(B)d1​(A)d1​(B).

The second milestone constructs and verifies one explicit CSS datum for which rZ+n+rX<d1(A)d1(B)r_Z+n+r_X<d_1(A)d_1(B)rZ​+n+rX​<d1​(A)d1​(B). This is the step that turns the general mechanism into an actual counterexample.

The capstone packages the construction as the direct existential statement

∃ A,Bd2(A⊗B)<min⁡0≤i≤2di(A)d2−i(B).\exists\,A,B\qquad d_2(A\otimes B)<\min_{0\le i\le 2}d_i(A)d_{2-i}(B).∃A,Bd2​(A⊗B)<0≤i≤2min​di​(A)d2−i​(B).

Thus the final theorem is not conditional on the existence of suitable code data: it exhibits finite based binary chain complexes for which the equality proposed in Conjecture 18 fails.

Relation to prior work

The counterexample concerns only the unrestricted passage from a one-complex factor to two arbitrary bounded complexes. It does not conflict with Zeng and Pryadko's Eq. (13), whose one-complex hypothesis rules out the three-bidegree interaction used here.

The broader literature also indicates why additional structure matters. Bravyi and Hastings introduced homological-product codes and analyzed logical representatives in product constructions; Audoux and Couvreur developed tensor products of CSS codes through chain-complex methods. More recently, Akhmechet et al. discussed the Zeng–Pryadko conjecture in the structured setting of complexes derived from Khovanov homology, while Berthusen et al. restated it as Conjecture 5.1 in their study of automorphism gadgets. Golowich and Guruswami obtained strong distance guarantees for iterated homological products under expansion and local-testability hypotheses. These results are compatible with the proposed counterexample: they concern special families or impose hypotheses that are absent from Conjecture 18.

Besides settling the unrestricted statement, the formalization isolates a reusable obstruction. It shows precisely how a low-weight class assembled across several bidegrees can evade a formula based only on the degreewise distances of the factors. This distinction should help guide corrected formulations in which an exact product formula, or a useful lower bound, is recovered from additional geometric, expansion, or local-testability assumptions.

The main formal issues are mathematically substantive rather than presentational: the proof must respect the endpoint conventions for the boundary maps, distinguish a cycle from a non-boundary, compare finite weights with ∞\infty∞-valued distances, and verify a concrete strict-gap instance. Making each of these points explicit is especially important here, because an indexing shift or a merely conditional existence statement would no longer constitute a refutation of the conjecture as stated.

References

  • W. Zeng and L. P. Pryadko, Higher-Dimensional Quantum Hypergraph-Product Codes with Finite Rates, Physical Review Letters 122, 230501 (2019); arXiv:1810.01519 (2018), Eq. (13).
  • W. Zeng and L. P. Pryadko, Minimal distances for certain quantum product codes and tensor products of chain complexes, Physical Review A 102, 062402 (2020); arXiv:2007.12152, Conjecture 18.
  • S. Bravyi and M. B. Hastings, Homological Product Codes, STOC 2014; arXiv:1311.0885.
  • B. Audoux and A. Couvreur, On tensor products of CSS codes, Annales de l'Institut Henri Poincaré D 6 (2019); arXiv:1512.07081.
  • R. Akhmechet et al., Khovanov homology and quantum error-correcting codes, arXiv:2410.11252 (2024).
  • N. Berthusen et al., Automorphism gadgets in homological product codes, arXiv:2508.04794 (2025).
  • L. Golowich and V. Guruswami, Quantum LDPC Codes of Almost Linear Distance via Iterated Homological Products, CCC 2025; full version.
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Complexity Theory·Captain: Goku

Shallow Quantum Circuits and Causal ConesTextbook

Motivation

A quantum circuit of depth ddd built from gates of fan-in at most two cannot let an output wire depend on more than 2d2^d2d input wires. The argument is folklore and takes a paragraph on paper: the causal cone of the measured wire grows by at most a factor of two per layer. Formalizing it exposes a subtlety that the paper argument hides, and that is what this mission is about.

The subtlety

Define the backward cone step of a wire set SSS through a layer lll by adjoining the support of every gate of lll that meets SSS. There is a choice here: test each gate against the incoming set SSS, or against the partially accumulated cone. Testing against the accumulator over-approximates, and the doubling bound fails. Testing against the incoming set gives the bound — but is only correct when the gates within a layer act on pairwise disjoint wires.

Without that hypothesis (LayerOk) the semantic statement is false, and the counterexample is small: on three wires, the single layer [cnot 2 1, cnot 1 0] has cone {0,1}\{0,1\}{0,1} around wire 000, yet wire 000 ends up holding x0⊕x1⊕x2x_0 \oplus x_1 \oplus x_2x0​⊕x1​⊕x2​. So the cone under-approximates the true dependence. This mission's development carries LayerOk throughout, and the counterexample is recorded in the source.

What is formalized

Layered circuits over {H,S,T,CNOT}\{H, S, T, \mathrm{CNOT}\}{H,S,T,CNOT} on nnn wires, with states as amplitude functions on bit-strings and no tensor products anywhere. On top of that:

  • the combinatorial half — one layer at most doubles the cone, hence ∣cone∣≤2d ∣S∣|\mathrm{cone}| \le 2^{d}\,|S|∣cone∣≤2d∣S∣;
  • norm preservation, so that acceptProb is a genuine probability in [0,1][0,1][0,1];
  • the semantic half — inputs agreeing on the causal cone of the output wire are accepted with equal probability.

The semantic half is proved in the Heisenberg picture. The measurement observable is conjugated backwards through the circuit and its support tracked: a gate meeting the support enlarges it by that gate's own wires, and a gate missing it commutes with the observable and cancels against its own adjoint. That cancellation is the reason the non-cascading cone step is correct, and it is why unitarity of the gate set is needed at the 2n2^n2n-dimensional level rather than gate by gate. Supporting this is a small reusable algebra of local operators: locality is monotone, closed under adjoint and product, and disjointly supported operators commute.

The frontier

The published depth bound assumes each input wire lies in the syntactic cone of the output. That is weaker than saying the wire matters. Milestone 1 asks for the semantically honest version, stated in terms of genuine functional dependence; the bridge is the semantic cone theorem already in the development.

Beyond that, the natural continuations are the same argument for fan-in-kkk gates (∣cone∣≤kd|\mathrm{cone}| \le k^{d}∣cone∣≤kd), for geometrically local circuits where cone growth is linear rather than exponential, and ultimately the Bravyi–Gosset–König separation QNC0⊄NC0\mathrm{QNC}^{0} \not\subset \mathrm{NC}^{0}QNC0⊂NC0 — which needs machinery (non-local games, magic squares) that this development deliberately does not build.

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Theoretical Computer Science·Captain: Goku

The Aaronson-Ambainis ConjectureOpen Problem

Motivation

Quantum query algorithms are known to beat classical ones on problems with algebraic structure -- period finding, hidden subgroups, forrelation. No such speedup is known for a problem with no structure at all. Aaronson and Ambainis proposed making that observation into a theorem, and reduced it to a question with no quantum content: a statement about bounded low-degree polynomials on the Boolean cube (Aaronson--Ambainis 2009).

The question has resisted since. A timeline of what is actually established:

  • 2009. Aaronson and Ambainis state the conjecture and prove that it implies almost-everywhere classical simulation of quantum query algorithms.
  • 2012. Montanaro settles the case of block-multilinear forms whose coefficients all have the same magnitude.
  • 2016. O'Donnell and Zhao reduce the general conjecture to a restricted class, the one-block decoupled polynomials.
  • 2019. Aaronson surveys a decade of partial progress (retrospective).
  • 2022. Bansal, Sinha and de Wolf prove the conjecture for completely bounded degree-ddd block-multilinear forms, obtaining influence 1/poly(d)1/\mathrm{poly}(d)1/poly(d) at constant variance (arXiv:2203.00212).
  • 2024. The conjecture is established for a non-negligible fraction of random restrictions (arXiv:2402.13952).

The cases that are settled are settled under structural hypotheses -- block-multilinearity, complete boundedness, symmetry, Boolean range. The general statement is open.

Setting

Let NNN be a positive integer. The Boolean cube is {0,1}N\{0,1\}^N{0,1}N, carrying the uniform distribution; a point xxx is identified with the 0/10/10/1 real vector it names, so a real multivariate polynomial ppp in NNN variables has a value p(x)p(x)p(x) at each cube point. For a function fff on the cube write

E[f]=2−N∑x∈{0,1}Nf(x).\mathbb{E}[f]=2^{-N}\sum_{x\in\{0,1\}^N}f(x).E[f]=2−Nx∈{0,1}N∑​f(x).

The variance of ppp is Var⁡[p]=E[(p−E[p])2]\operatorname{Var}[p]=\mathbb{E}\big[(p-\mathbb{E}[p])^2\big]Var[p]=E[(p−E[p])2]. Writing x⊕ix^{\oplus i}x⊕i for xxx with its iii-th bit flipped, the influence of coordinate iii on ppp is

Inf⁡i[p]=E[(p(x)−p(x⊕i))2].\operatorname{Inf}_i[p]=\mathbb{E}\big[(p(x)-p(x^{\oplus i}))^2\big].Infi​[p]=E[(p(x)−p(x⊕i))2].

These are the combinatorial forms of both quantities, as used in the source; no Fourier--Walsh expansion is required to state anything below. The degree of ppp is its total degree as a polynomial. Call ppp bounded when 0≤p(x)≤10\le p(x)\le 10≤p(x)≤1 at every cube point -- a condition imposed only on the cube, not on all of RN\mathbb{R}^NRN.

Target

The goal is the conjecture in the shape stated by its authors: there is an absolute constant CCC such that for all NNN, all ddd, every polynomial ppp of degree at most ddd that is bounded on the cube, and every ε>0\varepsilon>0ε>0 with Var⁡[p]≥ε\operatorname{Var}[p]\ge\varepsilonVar[p]≥ε, some coordinate iii satisfies

Inf⁡i[p]  ≥  (εd)C.\operatorname{Inf}_i[p]\;\ge\;\Big(\frac{\varepsilon}{d}\Big)^{C}.Infi​[p]≥(dε​)C.

The constant CCC is quantified outermost and may depend on nothing. That uniformity is the entire content: bounds that degrade exponentially in ddd are already known, and a goal naming a specific exponent would be superseded by the next improvement.

Significance

The result itself. Aaronson and Ambainis prove that the conjecture implies that the acceptance probability of any bounded-error TTT-query quantum algorithm on a Boolean input can be approximated, to small error on all but a small fraction of inputs, by a classical algorithm making poly(T)\mathrm{poly}(T)poly(T) queries. Quantum speedups would then require structure in a precise sense. The conjecture also has purely classical content, asserting that boundedness plus low degree forces variance to concentrate on some single coordinate rather than spread across all NNN. Without it, no such concentration is known at any rate polynomial in 1/d1/d1/d.

Formalizing it. The conjecture is open, so this mission does not formalize a known proof of the goal. What it produces is a machine-checked statement of the conjecture together with formalizations of the partial results above, each currently existing only on paper. The milestone chain also yields reusable infrastructure for analysis of Boolean functions, of which Mathlib currently contains none: no Fourier--Walsh expansion, no influence, no variance on the cube.

Difficulty

The elementary bound is the Poincare inequality on the cube, 4Var⁡[p]≤∑iInf⁡i[p]4\operatorname{Var}[p]\le\sum_i\operatorname{Inf}_i[p]4Var[p]≤∑i​Infi​[p], which yields a coordinate with influence at least 4ε/N4\varepsilon/N4ε/N. This is tight for the dictator p(x)=x1p(x)=x_1p(x)=x1​ and depends on NNN, so it says nothing: the conjecture demands a bound free of NNN entirely.

The natural repair is the route available when ppp takes only the values 000 and 111. A Boolean-valued polynomial of degree ddd depends on boundedly many coordinates, which immediately produces an influential one. That argument does not survive relaxing the range to the interval [0,1][0,1][0,1]: a bounded real-valued polynomial of low degree need not depend on boundedly many coordinates, and every known substitute loses a factor exponential in ddd. Closing the gap between exponential and polynomial dependence on ddd is the difficulty, and it is where all of the partial results stop.

Formalization scope

Polynomials are MvPolynomial (Fin N) ℝ and degree is Mathlib's totalDegree, so the statement needs no bespoke notion of degree. Expectation is a finite sum scaled by 2−N2^{-N}2−N rather than a measure-theoretic integral, keeping every definition elementary. Bit flipping is Function.update x i (!x i). Boundedness is asserted at cube points only. Variance and influence are the combinatorial definitions above, published as the definition AaronsonAmbainis.

Three points close off degenerate readings. The exponent O(1)O(1)O(1) of the source is rendered as an existentially quantified natural number with no leading multiplicative constant, since admitting one weakens the claim. Taking that exponent to be 000 would demand influence at least 111 and is therefore not a trivializing choice, while larger exponents only weaken the bound; the content is that some fixed exponent suffices for all NNN and ddd at once. The hypothesis deg⁡p≤d\deg p\le ddegp≤d is universally quantified over ddd, which is equivalent to the source's exact-degree form because the smallest admissible ddd gives the strongest conclusion. The cases N=0N=0N=0 and d=0d=0d=0 are vacuous, since 0<ε≤Var⁡[p]0<\varepsilon\le\operatorname{Var}[p]0<ε≤Var[p] fails for a constant polynomial.

A complete development needs, beyond the published definitions, a Fourier--Walsh layer with Parseval's identity, the level-kkk machinery used by the partial results, and -- for the completely bounded case -- operator-space norms on multilinear forms. All of the Boolean-analysis material is reusable well beyond this mission. Contributions of any milestone are welcome, as are alternative formalizations of the definitions in function-level rather than polynomial-level form.

Out of scope: the quantum simulation consequence is not formalized here. Stating it requires a formal quantum query model, which no Lean library currently provides.

Selected references

  • S. Aaronson, A. Ambainis, The Need for Structure in Quantum Speedups, Theory of Computing 10 (2014) 133--166; arXiv:0911.0996. Conjecture 6.
  • N. Bansal, M. Sinha, R. de Wolf, Influence in Completely Bounded Block-multilinear Forms and Classical Simulation of Quantum Algorithms, CCC 2022; arXiv:2203.00212.
  • Aaronson--Ambainis Conjecture Is True For Random Restrictions, 2024; arXiv:2402.13952.
  • S. Aaronson, The Aaronson-Ambainis Conjecture (2008-2019), blog retrospective.
  • S. Arunachalam, J. Briet, C. Palazuelos, Quantum query algorithms are completely bounded forms, SIAM J. Comput. 48 (2019); arXiv:1711.07285.
  • AIM problem list, Analysis on the hypercube with applications to quantum computing, aimpl.org/hypercubequantum.
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Zauner's Conjecture (SIC-POVMs)Open Problem

In a 1999 Vienna doctoral thesis, Gerhard Zauner conjectured that in every finite dimension d one can find d² unit vectors in complex d-space that are mutually as spread out as possible — any two sharing the same squared overlap 1/(d+1). Such a configuration, a symmetric informationally complete positive operator-valued measure (SIC-POVM), is the optimal minimal measurement for reconstructing an unknown quantum state, which is why the idea was rediscovered and named by Renes, Blume-Kohout, Scott, and Caves in 2004 and became central to quantum tomography, quantum cryptography, and the QBist reading of quantum mechanics. Geometrically these are maximal sets of complex equiangular lines; physically they are the most efficient quantum measurements; and, remarkably, they appear to be governed by deep number theory — recent work by Appleby, Flammia, Kopp, and others ties exact SICs to Stark units and Hilbert's twelfth problem on explicit class field theory. Exact solutions have been hand-built in scores of dimensions and numerical ones found in every dimension checked, yet a general existence proof remains out of reach. Formalizing Zauner's conjecture gives this problem — straddling quantum information, geometry, and algebraic number theory — a precise shared target.

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Category Theory·Captain: Bingyu Xia

Categorical Quantum Mechanics II: The Born RuleTextbook

Motivation

Quantum mechanics predicts probabilities, but it is notoriously quiet about what a probability is. Categorical quantum mechanics answers that by rewriting the finite-dimensional formalism in the language of dagger categories: a state is a morphism I→AI \to AI→A, an effect is a morphism A→IA \to IA→I, and the probability of an outcome is a scalar — an endomorphism of the tensor unit. On that translation the Born rule stops being an axiom and becomes a theorem about a complete, disjoint family of effects.

This mission formalizes that theorem, together with the two lemmas it rests on, in Lean 4 over Mathlib. It covers the dagger and measurement material of Chapter 2 of Reutter and Vicary's Categorical Quantum Mechanics.

Setting

Fix a monoidal dagger category C\mathcal{C}C with zero morphisms. The unit object III carries a commutative monoid structure End(I)\mathrm{End}(I)End(I) — the scalars. For a state a:I→ca : I \to ca:I→c and an effect x:c→Ix : c \to Ix:c→I, the probability that xxx occurs on aaa is the scalar

Prob(a,x)  =  a†∘x†∘x∘a.\mathrm{Prob}(a,x) \;=\; a^\dagger \circ x^\dagger \circ x \circ a .Prob(a,x)=a†∘x†∘x∘a.

A family of effects x:I→Eff(c)x : I \to \mathrm{Eff}(c)x:I→Eff(c) is complete when the induced map ⟨x⟩:⨁iI→c\langle x \rangle : \bigoplus_i I \to c⟨x⟩:⨁i​I→c satisfies ⋁ixi=idc\bigvee_i x_i = \mathrm{id}_c⋁i​xi​=idc​, and disjoint when xi†∘xj=0x_i^\dagger \circ x_j = 0xi†​∘xj​=0 for i≠ji \neq ji=j. Both conditions are stated for a dagger biproduct of the unit objects.

Formalization targets

Goal — the Born rule

∑iProb(a,xi)  =  idIfor x complete and disjoint\sum_{i} \mathrm{Prob}(a, x_i) \;=\; \mathrm{id}_I \qquad \text{for } x \text{ complete and disjoint}i∑​Prob(a,xi​)=idI​for x complete and disjoint

This is the mission's goal. It fixes nothing beyond completeness and disjointness; the statement is exactly the categorical Born rule for a finite outcome set.

Supporting results

  • Lemma 2.52. A family of effects is disjoint if and only if the dagger of its lift is an isometry; and complete if and only if the kernel of its lift is trivial.
  • Lemma 2.53. A complete and disjoint family of effects lifts to a unitary ⟨x⟩:⨁iI→c\langle x \rangle : \bigoplus_i I \to c⟨x⟩:⨁i​I→c.
  • Lemma 2.41, Corollary 2.42. Dagger biproducts: transposing a matrix of morphisms daggers every entry, and daggers distribute over addition.

Significance

The Born rule is the point where the categorical and the Hilbert-space pictures are reconciled: the abstract statement specialises, in Hilb\mathbf{Hilb}Hilb, to the usual ∑i∣⟨xi∣a⟩∣2=1\sum_i |\langle x_i | a \rangle|^2 = 1∑i​∣⟨xi​∣a⟩∣2=1. Proving it categorically means the rule is a consequence of the dagger-biproduct structure rather than an extra assumption, which is what makes the framework usable for quantum protocols — measurement, teleportation and the like are all built on complete disjoint families.

The supporting lemmas are reusable well beyond this mission: dagger biproducts are the categorical home of matrix calculus, and the isometry/unitary characterisations of disjointness and completeness are the standard toolkit for any later argument about measurements.

Difficulty

Moderate. The mathematics is elementary once the definitions are in place — the work is in bookkeeping: biproduct universal properties, the interaction of the dagger with the biproduct structure, and careful handling of the scalar monoid. The main intellectual step is realising that completeness alone, not equalizers, gives the second unitary identity.

Formalization scope

Formalized here: dagger categories and their morphism classes (§2.3), dagger biproducts (§2.3.3), and the scalar/state/effect vocabulary with the Born rule (§2.4.3). Mathlib has no dagger-category theory at all, so the definitions are supplied from scratch as Lean Definitions and are importable independently of this mission.

Not formalized: the Hilbert-space and relational models, the graphical calculus of §2.2, and the measurement/post-processing material after §2.4.3.

Two corrections to the source are made and documented in the formalization. The printed statement of Proposition 2.55 assumes completeness only, which is false; disjointness is required, as the book's own proof (which invokes Lemma 2.52) already assumes. And the book attributes the identity x†∘x=idx^\dagger \circ x = \mathrm{id}x†∘x=id on AAA to Lemma 2.52, whereas Lemma 2.52 only gives the identity on ⨁iI\bigoplus_i I⨁i​I; the identity on AAA is Lemma 2.53. Lemma 2.53 is proved here without the book's equalizer hypothesis, so it is strictly stronger than the printed version.

Selected references

  • D. Reutter and J. Vicary, Categorical Quantum Mechanics, §2.3.3 and §2.4.3.
  • S. Abramsky and B. Coecke, A categorical semantics of quantum protocols, LICS 2004.
  • The Mathlib CategoryTheory.Limits.Biproducts and CategoryTheory.Monoidal.Category API, on which the definitions are built.
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Optimization·Captain: Goku

Oracle-Parameterized Convergence Rates: SPIDER, Q-SPIDER, and the Exact CrossoverResearch Paper

Motivation

Quantum algorithms for stochastic optimization are usually presented one paper at a time: a schedule is fixed, a quantum mean estimator is substituted for a classical minibatch, and a new rate is derived from scratch. The derivations are near-identical, and the step that actually differs — the price of one gradient query — is buried inside each proof rather than exposed as a parameter.

This mission publishes a Lean 4 development in which the oracle is a parameter, not an assumption. One rate theorem, instantiated at different oracle contracts and cost models, yields the classical rate, the inexact-gradient rate, and the quantum rate. All constants are explicit; nothing is asymptotic.

The published results it reproduces or corrects:

  • Ghadimi--Lan (2013), the ε−4\varepsilon^{-4}ε−4 rate for smooth nonconvex SGD.
  • Fang et al., the classical SPIDER variance-reduction schedule and its ε−3\varepsilon^{-3}ε−3 query complexity.
  • Sidford--Zhang, Quantum speedups for stochastic optimization (arXiv:2308.01582) — Theorem 6's O~(Δℓσd ε−3)\tilde O(\Delta\ell\sigma\sqrt{d}\,\varepsilon^{-3})O~(Δℓσd​ε−3) and Theorem 8's O~(ℓΔdσ ε−5/2)\tilde O(\ell\Delta\sqrt{d\sigma}\,\varepsilon^{-5/2})O~(ℓΔdσ​ε−5/2), both obtained here from one schedule evaluated at two cost exponents.

Setting

Let EEE be a real inner-product space, f:E→Rf:E\to\mathbb{R}f:E→R an objective, and g:E→Eg:E\to Eg:E→E a map supplied as a parameter in place of the gradient. The smoothness hypothesis is the descent-lemma inequality

f(y)  ≤  f(x)+⟨g(x),y−x⟩+L2∥y−x∥2,f(y)\;\le\;f(x)+\langle g(x),y-x\rangle+\tfrac{L}{2}\|y-x\|^{2},f(y)≤f(x)+⟨g(x),y−x⟩+2L​∥y−x∥2,

written QuadUpper f g L\mathrm{QuadUpper}\,f\,g\,LQuadUpperfgL; this is exactly what rate proofs consume, and it is implied by a Lipschitz gradient. Write Δ0=f(x0)−f⋆\Delta_0=f(x_0)-f^{\star}Δ0​=f(x0​)−f⋆ for the initial gap, ε\varepsilonε for the target accuracy, σ\sigmaσ for the gradient-noise scale, ℓ\ellℓ for the mean-squared smoothness constant, and ddd for the ambient dimension.

A cost model converts a target accuracy into a query count as a power law with exponent ppp. Its p=2p=2p=2 member is the classical minibatch bill, scaling as σ2/ε2\sigma^{2}/\varepsilon^{2}σ2/ε2; its p=1p=1p=1 member is the quantum mean-estimation bill, scaling as σ/ε\sigma/\varepsilonσ/ε. That single exponent is where classical and quantum part company.

Target

The goal theorem is the exact crossover between the two SPIDER bills. Writing QQQ and CCC for the dominant terms of the quantum and classical query totals,

Q=64000 ℓΔd10σε2ε,C=25 728 000 ℓΔσε3,Q=\frac{64000\,\ell\Delta\sqrt{d}\sqrt{10\sigma}}{\varepsilon^{2}\sqrt{\varepsilon}},\qquad C=\frac{25\,728\,000\,\ell\Delta\sigma}{\varepsilon^{3}},Q=ε2ε​64000ℓΔd​10σ​​,C=ε325728000ℓΔσ​,

the target asserts, for ℓ,Δ,σ,ε>0\ell,\Delta,\sigma,\varepsilon>0ℓ,Δ,σ,ε>0 and d≥0d\ge0d≥0,

Q<C⟺d ε<16000 σ.Q<C\quad\Longleftrightarrow\quad d\,\varepsilon<16000\,\sigma .Q<C⟺dε<16000σ.

Every supporting rate is also published and proved: the two SPIDER query totals, SPIDER's correctness, the SGD and PL rates, the exact and inexact gradient-descent rates, the two variance-purchase bills, and the two query counts.

Significance

The results. The crossover makes the dimension-versus-accuracy trade-off of quantum stochastic optimization quantitative rather than folkloric. Two readings follow directly: at fixed ddd the quantum advantage disappears as ε→0\varepsilon\to0ε→0, so the speedup lives at moderate accuracy, not asymptotically; and at fixed ε\varepsilonε the advantage requires d<16000σ/εd<16000\sigma/\varepsilond<16000σ/ε. Note what cancels — ℓ\ellℓ, Δ\DeltaΔ and the ε\varepsilonε-exponent all drop out, leaving only dεd\varepsilondε against σ\sigmaσ.

The formalization. Because the oracle and the cost exponent are parameters, the classical and quantum rates are one theorem evaluated twice rather than two proofs. This mission is unusual in that its frontier is already closed: every node arrives with a machine-checked proof, transplanted from a green build. What it offers the platform is a reusable, fully-proved layer for first-order convergence analysis — function classes, cost models, a one-step descent recursion, accumulation laws including a stopped-time version, and the SPIDER schedule — on which further rates can be built by instantiation.

Difficulty

The apparent difficulty is not where a newcomer expects. Deriving a rate from the one-step recursion is routine telescoping. What is delicate is keeping the constants honest while the oracle varies: a rate proof that quietly assumes an exact gradient, or a global lower bound on fff, will produce the right-looking exponent from the wrong hypotheses.

Two specific places carry real content. Evaluating an error recursion at a random return time breaks the unconditional variance bound, because conditioning on τ=k\tau=kτ=k destroys independence; the stopped-time accumulation law is what repairs it. And reproducing a published constant exactly — rather than up to O~(⋅)\tilde O(\cdot)O~(⋅) — is what certifies that the parametrized machinery has not silently degraded the bound it generalizes.

Formalization scope

Smoothness is QuadUpper on an explicitly supplied g; no differentiability or convexity is assumed anywhere, and the only lower-bound hypothesis is f⋆≤f(xK)f^{\star}\le f(x_K)f⋆≤f(xK​) at the terminal iterate rather than globally. Cost models are an inductive family with a power-law member, so the classical and quantum instances are p=2p=2p=2 and p=1p=1p=1 of one definition. Half-integer powers are written with Real.sqrt, so no real exponentiation appears in any statement. Stochastic results use a genuine Filtration and a conditional oracle contract; the tower property is derived, not assumed.

Two honesty notes. Several statements carry hypotheses that Lean marks unused; these are recorded as such in the individual nodes rather than presented as load-bearing. And the library records a discrepancy in Sidford--Zhang's Algorithm 7 parameter block, documented in its own STATUS notes; the formalization follows the corrected parameters.

Selected references

  • S. Bubeck-style descent machinery aside, the rates reproduced here are: S. Ghadimi and G. Lan, Stochastic first- and zeroth-order methods for nonconvex stochastic programming, SIAM J. Optim. 23(4) (2013).
  • C. Fang, C. J. Li, Z. Lin, T. Zhang, SPIDER: Near-optimal non-convex optimization via stochastic path-integrated differential estimator, NeurIPS 2018.
  • A. Sidford and C. Zhang, Quantum speedups for stochastic optimization, arXiv:2308.01582.
  • Source development: lean-optrates, github.com/shiy1022/lean-optrates at commit 4c0b8498, Apache-2.0, by Yueheng Shi. The platform copy renames the root namespace OptRates to ShiOptRates; no statement or proof is otherwise altered.
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Algebraic TopologyInformation TheoryQuantum Error Correction·Captain: Rui Chao

Higher-Dimensional Quantum Hypergraph-Product Codes with Finite RatesResearch Paper

Motivation

Quantum low-density parity-check codes encode quantum information using sparse parity constraints. A standard way to construct them is to translate binary chain complexes into Calderbank--Shor--Steane codes and to combine complexes by tensor product. Homology identifies the logical operators of the resulting code, while the smallest Hamming weight of a nontrivial homology class controls one of its distances. Determining how this distance behaves under a tensor product is therefore a basic structural question, not merely a parameter calculation.

Weilei Zeng and Leonid P. Pryadko studied products in which one factor is an arbitrary finite binary chain complex and the other is the one-complex induced by a binary matrix. Their paper was published as “Higher-Dimensional Quantum Hypergraph-Product Codes with Finite Rates,” Physical Review Letters 122, 230501 (2019). Its main distance result is Eq. (13) in the arXiv version: for this particular tensor factor, the usual product upper bound is always exact. The result extends the familiar two-complex setting of quantum hypergraph-product codes to the local structure occurring in complexes of any dimension.

Setting

A based binary chain complex consists of finite-dimensional vector spaces AiA_iAi​ over F2\mathbb F_2F2​, each equipped with a specified coordinate basis, and linear boundary maps

⋯⟶Ai+1→∂i+1Ai→∂iAi−1⟶⋯\cdots\longrightarrow A_{i+1}\xrightarrow{\partial_{i+1}}A_i \xrightarrow{\partial_i}A_{i-1}\longrightarrow\cdots⋯⟶Ai+1​∂i+1​​Ai​∂i​​Ai−1​⟶⋯

such that ∂i∂i+1=0\partial_i\partial_{i+1}=0∂i​∂i+1​=0. Its degree-iii homology is Hi(A)=ker⁡∂i/im⁡∂i+1H_i(\mathcal A)=\ker\partial_i/\operatorname{im}\partial_{i+1}Hi​(A)=ker∂i​/im∂i+1​. The homological distance is measured in the chosen basis:

di(A)=min⁡{wt⁡(x):x∈ker⁡∂i∖im⁡∂i+1}.d_i(\mathcal A)= \min\{\operatorname{wt}(x):x\in\ker\partial_i\setminus \operatorname{im}\partial_{i+1}\}.di​(A)=min{wt(x):x∈ker∂i​∖im∂i+1​}.

Following the paper, the minimum of an empty set is ∞\infty∞. Thus di(A)=∞d_i(\mathcal A)=\inftydi​(A)=∞ when Hi(A)H_i(\mathcal A)Hi​(A) is trivial.

The endpoint convention is also the one stated explicitly after Eq. (1). For an mmm-complex, ∂0:A0→{0}\partial_0:A_0\to\{0\}∂0​:A0​→{0} is the zero 0×n00\times n_00×n0​ matrix and ∂m+1:{0}→Am\partial_{m+1}:\{0\}\to A_m∂m+1​:{0}→Am​ is the zero nm×0n_m\times0nm​×0 matrix. Consequently

d0(A)=min⁡{wt⁡(x):x∈A0∖im⁡∂1}d_0(\mathcal A)=\min\{\operatorname{wt}(x): x\in A_0\setminus\operatorname{im}\partial_1\}d0​(A)=min{wt(x):x∈A0​∖im∂1​}

and

dm(A)=min⁡{wt⁡(x):0≠x∈ker⁡∂m}.d_m(\mathcal A)=\min\{\operatorname{wt}(x): 0\ne x\in\ker\partial_m\}.dm​(A)=min{wt(x):0=x∈ker∂m​}.

For an r×cr\times cr×c binary matrix PPP, the one-complex K(P)\mathcal K(P)K(P) has F2c\mathbb F_2^cF2c​ in degree one, F2r\mathbb F_2^rF2r​ in degree zero, and boundary PPP. Its two distances are

d1(K(P))=min⁡{wt⁡(x):Px=0, x≠0}d_1(\mathcal K(P))= \min\{\operatorname{wt}(x):Px=0,\ x\ne0\}d1​(K(P))=min{wt(x):Px=0, x=0}

and

d0(K(P))=min⁡{wt⁡(y):y∉im⁡P}.d_0(\mathcal K(P))= \min\{\operatorname{wt}(y):y\notin\operatorname{im}P\}.d0​(K(P))=min{wt(y):y∈/imP}.

In particular, d0=1d_0=1d0​=1 unless PPP has full row rank, in which case d0=∞d_0=\inftyd0​=∞. The degree-jjj chain group of A×K(P)\mathcal A\times\mathcal K(P)A×K(P) is

(Aj⊗F2r)⊕(Aj−1⊗F2c),(A_j\otimes\mathbb F_2^r)\oplus (A_{j-1}\otimes\mathbb F_2^c),(Aj​⊗F2r​)⊕(Aj−1​⊗F2c​),

with the standard tensor-product boundary. Over F2\mathbb F_2F2​ the usual sign in that boundary has no effect.

Formalization targets

Tensor-product upper bound for arbitrary complexes

The first milestone is Eq. (11) for two arbitrary finite-length based binary chain complexes:

dj(A×B)≤min⁡idi(A)dj−i(B).d_j(\mathcal A\times\mathcal B)\le \min_i d_i(\mathcal A)d_{j-i}(\mathcal B).dj​(A×B)≤imin​di​(A)dj−i​(B).

Rank-sensitive lower bound

Let u=rank⁡Pu=\operatorname{rank}Pu=rankP and δ=d1(K(P))\delta=d_1(\mathcal K(P))δ=d1​(K(P)). The second milestone is Theorem 1, including both of its cases:

u<r⟹dj(A×K(P))≥min⁡ ⁣(dj(A),dj−1(A)δ),u<r\Longrightarrow d_j(\mathcal A\times\mathcal K(P))\ge \min\!\left(d_j(\mathcal A),d_{j-1}(\mathcal A)\delta\right),u<r⟹dj​(A×K(P))≥min(dj​(A),dj−1​(A)δ),

and

u=r⟹dj(A×K(P))≥dj−1(A)δ.u=r\Longrightarrow d_j(\mathcal A\times\mathcal K(P))\ge d_{j-1}(\mathcal A)\delta.u=r⟹dj​(A×K(P))≥dj−1​(A)δ.

Exact distance with a one-complex

The goal is Eq. (13):

dj(A×K(P))=min⁡ ⁣(dj−1(A)d1(K(P)),dj(A)d0(K(P))).d_j(\mathcal A\times\mathcal K(P))= \min\!\left( d_{j-1}(\mathcal A)d_1(\mathcal K(P)), d_j(\mathcal A)d_0(\mathcal K(P)) \right).dj​(A×K(P))=min(dj−1​(A)d1​(K(P)),dj​(A)d0​(K(P))).

No full-rank hypothesis is imposed on PPP.

Significance

The equality determines the product distance exactly from four component distances. General tensor-product arguments immediately provide the upper bound, but an exact formula requires ruling out lower-weight homology classes that mix the two direct-sum blocks. Once established, the formula can be applied repeatedly to tensor products of one-complexes, which is the step used in the paper to obtain higher-dimensional quantum hypergraph-product code families and to compute their distances.

For formalization, the mission contributes reusable definitions of finite based binary chain data, homological distance valued in N∪{∞}\mathbb N\cup\{\infty\}N∪{∞}, the one-complex of a binary matrix, and the relevant tensor-product boundary maps. Mathlib contains Hamming weight and general homological-algebra infrastructure, while QECLean contains a closely related based length-three homological-code interface. Neither the selected Mathlib environment nor the inspected QECLean development currently supplies this rank-sensitive exact distance theorem.

Difficulty

The central issue is that Hamming weight depends on the chosen bases and is not preserved by arbitrary homological isomorphisms. A Künneth isomorphism describes the product homology and readily produces low-weight representatives, which is enough for the upper bound, but it does not by itself exclude a still lighter representative obtained by cancellation between the two tensor blocks. The lower bound must also remain valid at the endpoints of the complex and in singular cases where one or more homology groups vanish and the relevant distance is ∞\infty∞.

The theorem cannot be reduced to a dimension calculation. It must reason about supports and Hamming weights of based representatives while respecting the quotient by boundaries, and it must cover both rank⁡P<r\operatorname{rank}P<rrankP<r and rank⁡P=r\operatorname{rank}P=rrankP=r.

Formalization scope

The Lean development works over ZMod 2. A finite basis in degree iii is represented by Fin (dimension i), and a chain group is the function space from that coordinate type to ZMod 2. BasedBinaryChainComplex stores the dimension and boundary in every nonnegative degree, the chain condition, and a finite length above which all dimensions are zero. Thus the first milestone quantifies over genuinely arbitrary finite lengths for both A\mathcal AA and B\mathcal BB, rather than over a local window or a one-complex specialization. If the stored length is mmm, the zero-dimensional source in degree m+1m+1m+1 makes ∂m+1:{0}→Am\partial_{m+1}:\{0\}\to A_m∂m+1​:{0}→Am​ the unique zero map, just as the zero-dimensional target below degree zero makes ∂0:A0→{0}\partial_0:A_0\to\{0\}∂0​:A0​→{0} the unique zero map. Hence both singular endpoint cases in Eqs. (1) and (4) are represented directly.

Distances use WithTop ℕ. Their definitions are actual minima of Hamming weights of nontrivial representatives, with ⊤ produced by the empty-set case; infinite distance is not an extra hypothesis or a separately hard-coded branch. Coordinate types may be empty, which covers missing endpoint blocks. The binary matrix PPP is represented as a linear map between two finite based function spaces. Its row and column coordinate types need not be nonempty, and no injectivity or surjectivity assumption is added.

The degree-jjj product group is indexed by the disjoint union of all coordinate products Ai×Bj−iA_i\times B_{j-i}Ai​×Bj−i​ for 0≤i≤j0\le i\le j0≤i≤j. Consequently its Hamming norm is the sum of the weights of all tensor-degree blocks. The product boundary is the standard signed tensor boundary; its sign disappears over F2\mathbb F_2F2​. A formal proof verifies that every pair of consecutive product boundaries composes to zero; the cancellation of the two mixed terms uses characteristic two. Thus the product distance is taken from an actual chain complex, rather than from unrelated adjacent linear maps. A basis-free tensor product or an abstract homology group alone is insufficient for the target, because either would discard the weight data on which the statement depends. The mission does not formalize the asymptotic code-family construction later in the paper, the transposed cohomological distance, or the CSS-code parameter translation. Those are natural downstream missions; they should reuse rather than alter the present based-chain definitions.

Selected references

  • Weilei Zeng and Leonid P. Pryadko, “Higher-Dimensional Quantum Hypergraph-Product Codes with Finite Rates,” Physical Review Letters 122, 230501 (2019). arXiv:1810.01519.
  • Benjamin Audoux and Alain Couvreur, “On Tensor Products of CSS Codes,” arXiv:1512.07081 (2015), especially Proposition 1.13 and Corollary 2.14 as cited by Zeng--Pryadko.
  • Jean-Pierre Tillich and Gilles Zémor, “Quantum LDPC Codes With Positive Rate and Minimum Distance Proportional to the Square Root of the Blocklength,” IEEE Transactions on Information Theory 60 (2014), 1193--1202.
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Captain: Elsie66

Grover's AlgorithmResearch Paper

Motivation

Searching an unsorted list of NNN items for a single marked entry takes Θ(N)\Theta(N)Θ(N) queries classically — there is no way to do better than checking items one at a time. Grover's algorithm (Grover 1996) shows that a quantum computer solves the same problem in Θ(N)\Theta(\sqrt N)Θ(N​) queries, a quadratic speedup that applies to any problem expressible as unstructured search over a black-box oracle (this includes brute-forcing NP-complete problems and inverting one-way functions, which is why post-quantum cryptography doubles key lengths to compensate). Unlike Shor's algorithm, Grover's algorithm is provably optimal: Bennett–Bernstein–Brassard–Vazirani (1997) showed Ω(N)\Omega(\sqrt N)Ω(N​) queries are necessary for any quantum algorithm solving unstructured search, so the quadratic speedup is the best any quantum algorithm can achieve on this problem.

Setting

Model an NNN-item database as the standard basis of E=CNE = \mathbb{C}^NE=CN (EuclideanSpace ℂ (Fin N)), with inner product ⟨x,y⟩=∑ixi‾ yi\langle x,y\rangle = \sum_i \overline{x_i}\,y_i⟨x,y⟩=∑i​xi​​yi​. Fix a marked index w0∈{0,…,N−1}w_0 \in \{0,\dots,N-1\}w0​∈{0,…,N−1}. The algorithm starts in the uniform superposition

∣s⟩=1N∑i∣i⟩,|s\rangle = \frac{1}{\sqrt N}\sum_{i} |i\rangle,∣s⟩=N​1​i∑​∣i⟩,

a unit vector assigning equal amplitude to every item. Two reflections drive the search:

  • the oracle O=I−2∣w0⟩⟨w0∣O = I - 2|w_0\rangle\langle w_0|O=I−2∣w0​⟩⟨w0​∣, which flips the sign of the amplitude on the marked item and leaves every other basis state fixed;
  • the diffusion operator D=2∣s⟩⟨s∣−ID = 2|s\rangle\langle s| - ID=2∣s⟩⟨s∣−I ("inversion about the mean"), the reflection about ∣s⟩|s\rangle∣s⟩.

One Grover iterate is G=D OG = D\,OG=DO. The algorithm applies GGG some number of times to ∣s⟩|s\rangle∣s⟩ and measures; a measurement outcome equal to w0w_0w0​ counts as success.

Formalization targets

Milestone — the iterate is an isometry

∥Gx∥=∥x∥for every x∈E\|G x\| = \|x\| \quad \text{for every } x \in E∥Gx∥=∥x∥for every x∈E

OOO and DDD are each reflections about a unit vector, hence isometries; their composition GGG is therefore norm-preserving on the whole space, not just at ∣s⟩|s\rangle∣s⟩ — the minimal fact needed for GGG to be a legitimate quantum operation.

Milestone — the rotation formula

⟨w0,Gks⟩=sin⁡((2k+1)θ),θ:=arcsin⁡ ⁣(1N)\langle w_0, G^k s\rangle = \sin\bigl((2k+1)\theta\bigr), \qquad \theta := \arcsin\!\left(\tfrac{1}{\sqrt N}\right)⟨w0​,Gks⟩=sin((2k+1)θ),θ:=arcsin(N​1​)

The geometric heart of the algorithm (Nielsen & Chuang, Quantum Computation and Quantum Information, Section 6.1.2): restricted to the real two-dimensional subspace spanned by ∣w0⟩|w_0\rangle∣w0​⟩ and the component of ∣s⟩|s\rangle∣s⟩ orthogonal to it, GGG acts as rotation by a fixed angle 2θ2\theta2θ. Each iterate therefore advances the amplitude on the marked state along sin⁡((2k+1)θ)\sin((2k+1)\theta)sin((2k+1)θ), exactly as claimed, with θ=arcsin⁡(1/N)\theta = \arcsin(1/\sqrt N)θ=arcsin(1/N​) the rotation's initial offset (since ⟨w0,s⟩=1/N\langle w_0, s\rangle = 1/\sqrt N⟨w0​,s⟩=1/N​ at k=0k=0k=0).

Goal

∃ k,1−1N  ≤  ∣⟨w0,Gks⟩∣2\exists\, k,\quad 1 - \tfrac1N \;\le\; \bigl|\langle w_0, G^k s\rangle\bigr|^2∃k,1−N1​≤​⟨w0​,Gks⟩​2

Some number of iterations drives the probability of measuring the marked item above 1−1/N1-1/N1−1/N. The goal is stated existentially, without fixing kkk to a specific rounded formula: the rotation angle (2k+1)θ(2k+1)\theta(2k+1)θ can be made to land within θ\thetaθ of π/2\pi/2π/2 by an appropriate integer kkk, and at that point sin⁡2((2k+1)θ)≥cos⁡2θ=1−sin⁡2θ=1−1/N\sin^2((2k+1)\theta) \ge \cos^2\theta = 1-\sin^2\theta = 1 - 1/Nsin2((2k+1)θ)≥cos2θ=1−sin2θ=1−1/N. Pinning kkk down to an explicit closed form (e.g. the nearest integer to π/(4θ)−1/2\pi/(4\theta) - 1/2π/(4θ)−1/2) is one valid strategy, but is not required by the statement — any correct choice of kkk, and any correct proof it works, closes the goal.

Significance

Grover's algorithm is the second landmark quantum algorithm after Shor's, and the one with the widest applicability: because it treats the search space as a black box, it accelerates any brute-force search — SAT solving, collision finding, and generic key search among them — which is the concrete reason NIST's post-quantum cryptography standards double symmetric key lengths rather than replacing them outright. The mathematics itself has been fully settled since 1996, including matching optimality lower bounds; nothing here is open. What this mission adds is a machine- checked derivation of the amplitude formula and success bound directly from the definitions of the oracle and diffusion operators as concrete linear operators on EuclideanSpace ℂ (Fin N) — Mathlib has the finite-dimensional inner product space and rank-one operator machinery this needs (InnerProductSpace.rankOne, EuclideanSpace.single), but no existing formalization of the algorithm itself.

Difficulty

The obvious first attempt tries to track the full NNN-dimensional state vector through kkk iterations. This is intractable in general: GGG's action on an arbitrary basis vector depends on its overlap with both ∣w0⟩|w_0\rangle∣w0​⟩ and ∣s⟩|s\rangle∣s⟩. The move that makes the problem tractable is recognizing that GGG preserves the two-dimensional real subspace span{∣w0⟩,∣s⟩}\mathrm{span}\{|w_0\rangle, |s\rangle\}span{∣w0​⟩,∣s⟩} — everything orthogonal to this plane is fixed by both OOO and DDD, and inside the plane GGG is exactly a rotation matrix by angle 2θ2\theta2θ. Establishing this invariance and then tracking only the rotation angle (rather than the full vector) is the standard reduction, and the one this mission's milestones are built around; skipping it and attempting a direct NNN-dimensional induction does not scale.

Formalization scope

Works over a general N:NN:\mathbb NN:N together with a marked index w0:Fin Nw_0 : \mathrm{Fin}\,Nw0​:FinN — no assumption that NNN is a power of two, since the rotation argument is agnostic to how the NNN basis states are physically encoded into qubits (that encoding is a separate, unrelated concern from the search dynamics proved here). Supplying w0 : Fin N already forces N≥1N \ge 1N≥1; no separate nonemptiness hypothesis is added. The oracle and diffusion operators are built directly from Mathlib's InnerProductSpace.rankOne rather than an ad-hoc pointwise definition, so their reflection structure (and hence unitarity) is visible from the definition itself. A trivializing formalization is ruled out explicitly: the goal is stated as an existential over kkk rather than a fixed closed-form iteration count, so a correct proof must still exhibit a genuine successful kkk and establish the bound — it cannot be discharged by an unrelated or degenerate choice. Contributions extending this to multiple marked items, or proving the matching Ω(N)\Omega(\sqrt N)Ω(N​) lower bound (Bennett–Bernstein–Brassard–Vazirani 1997), are welcome as follow-up missions.

Selected references

  • L. K. Grover, A fast quantum mechanical algorithm for database search, STOC 1996. https://arxiv.org/abs/quant-ph/9605043
  • M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information, Cambridge University Press, 2000, Section 6.1.
  • C. H. Bennett, E. Bernstein, G. Brassard, and U. Vazirani, Strengths and Weaknesses of Quantum Computing, SIAM J. Comput. 26 (1997). https://arxiv.org/abs/quant-ph/9701001
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Mathematical Physics·Captain: Lucas

Peres-Terno: the no-communication theorem for commuting Kraus operatorsResearch Paper

Motivation

Two observers, Alice and Bob, share a quantum system. Alice performs some intervention (a measurement, possibly followed by discarding part of her apparatus) and Bob, somewhere else, performs his own. If the statistics of Bob's outcomes could depend on what Alice chose to do, Alice could send Bob a message through the shared system alone, and if the two interventions are spacelike separated this would be a signal faster than light. Quantum mechanics and special relativity coexist peacefully only because this does not happen: the no-communication theorem guarantees that Bob's marginal statistics are independent of Alice's intervention whenever the two interventions are "local" to different parts of the system.

The review article of Peres and Terno, Quantum information and relativity theory (Rev. Mod. Phys. 76, 2004), develops the operational toolkit of quantum information (Kraus matrices, positive-operator-valued measures, completely positive maps) in Sec. II.D and then derives, in Sec. II.E, a sufficient algebraic condition for the absence of instantaneous information transfer: all of Alice's Kraus matrices commute with all of Bob's (their Eq. (9)). This mission formalizes that derivation together with the facts about Kraus matrices and complete positivity that the same pages state.

Setting

A quantum state on a finite-dimensional Hilbert space Cd\mathbb C^dCd is a density matrix ρ\rhoρ: a positive semidefinite d×dd\times dd×d complex matrix with tr⁡ρ=1\operatorname{tr}\rho = 1trρ=1.

An intervention with outcomes μ\muμ is described by Kraus matrices AμmA_{\mu m}Aμm​, where the label mmm ranges over a finite set indexing the subsystems discarded at the end of the interaction. When outcome μ\muμ occurs, the state is updated (Eq. (6)) to the unnormalized matrix

ρμ′=∑mAμm ρ Aμm†,\rho'_\mu = \sum_m A_{\mu m}\,\rho\,A_{\mu m}^\dagger ,ρμ′​=m∑​Aμm​ρAμm†​,

and the probability of outcome μ\muμ is pμ=tr⁡ρμ′p_\mu = \operatorname{tr}\rho'_\mupμ​=trρμ′​. The matrices

Eμ=∑mAμm†AμmE_\mu = \sum_m A_{\mu m}^\dagger A_{\mu m}Eμ​=m∑​Aμm†​Aμm​

(Eq. (8)) are the POVM elements; the intervention is complete when ∑μEμ=1\sum_\mu E_\mu = \mathbb 1∑μ​Eμ​=1.

A map TTT on matrices is positive if it sends positive semidefinite matrices to positive semidefinite matrices, and completely positive if T⊗1T\otimes\mathbb 1T⊗1, acting blockwise on matrices over Cd⊗Cn\mathbb C^d\otimes\mathbb C^nCd⊗Cn, is positive for every ancilla dimension nnn.

For the no-communication theorem, Alice's Kraus matrices AμmA_{\mu m}Aμm​ and Bob's Kraus matrices BνnB_{\nu n}Bνn​ act on the same finite-dimensional space. The probability that Bob obtains ν\nuν, irrespective of Alice's outcome, is (Eq. (10))

pν=∑μtr⁡(∑m,nBνnAμm ρ Aμm†Bνn†).p_\nu = \sum_\mu \operatorname{tr}\Big(\sum_{m,n} B_{\nu n} A_{\mu m}\,\rho\,A_{\mu m}^\dagger B_{\nu n}^\dagger\Big).pν​=μ∑​tr(m,n∑​Bνn​Aμm​ρAμm†​Bνn†​).

Formalization targets

Goal: no-communication (Sec. II.E, Eqs. (9)-(11))

If [Aμm,Bνn]=0[A_{\mu m}, B_{\nu n}] = 0[Aμm​,Bνn​]=0 for all μ,m,ν,n\mu, m, \nu, nμ,m,ν,n, Alice's POVM is complete, and ρ\rhoρ is a density matrix, then for every outcome ν\nuν of Bob

∑μtr⁡(∑m,nBνnAμm ρ Aμm†Bνn†)=tr⁡(∑nBνn ρ Bνn†),\sum_\mu \operatorname{tr}\Big(\sum_{m,n} B_{\nu n} A_{\mu m}\,\rho\,A_{\mu m}^\dagger B_{\nu n}^\dagger\Big) = \operatorname{tr}\Big(\sum_n B_{\nu n}\,\rho\,B_{\nu n}^\dagger\Big),μ∑​tr(m,n∑​Bνn​Aμm​ρAμm†​Bνn†​)=tr(n∑​Bνn​ρBνn†​),

so all of Alice's operators disappear from Bob's statistics.

Milestones (Sec. II.D-II.E)

  1. Eq. (7): ∑mtr⁡(AμmρAμm†)=tr⁡(ρEμ)\sum_m \operatorname{tr}(A_{\mu m}\rho A_{\mu m}^\dagger) = \operatorname{tr}(\rho E_\mu)∑m​tr(Aμm​ρAμm†​)=tr(ρEμ​).
  2. Eq. (8): every EμE_\muEμ​ is positive semidefinite.
  3. Eqs. (7)-(8): for a density matrix and a complete POVM, the pμp_\mupμ​ are nonnegative reals summing to 111.
  4. Eq. (6) defines a completely positive map.
  5. Eq. (6) is the most general completely positive linear map: every completely positive linear map between matrix algebras has a finite Kraus representation.
  6. Time reversal (transposition, i.e. complex conjugation of a Hermitian ρ\rhoρ) is a positive map,
  7. but it is not completely positive.
  8. The exchange step of Sec. II.E: under the commutation hypothesis, tr⁡∑m,nBνnAμmρAμm†Bνn†=tr⁡(Eμ∑nBνnρBνn†)\operatorname{tr}\sum_{m,n} B_{\nu n}A_{\mu m}\rho A_{\mu m}^\dagger B_{\nu n}^\dagger = \operatorname{tr}\big(E_\mu \sum_n B_{\nu n}\rho B_{\nu n}^\dagger\big)tr∑m,n​Bνn​Aμm​ρAμm†​Bνn†​=tr(Eμ​∑n​Bνn​ρBνn†​).

Significance

The no-communication theorem is the consistency check between quantum measurement theory and relativistic causality, and it is the starting point of the relativistic measurement theory developed in Sec. III of the review. The commutation condition (9) is exactly what local quantum field theory provides for spacelike separated regions, so this algebraic form is the one later used for microcausality arguments. The Kraus representation (milestones 4-5) is a basic structural theorem of quantum information theory, and the failure of complete positivity for transposition (milestones 6-7) underlies the partial-transpose entanglement criterion.

The results are classical and their proofs are known; the mission produces machine-checked versions on top of Mathlib's matrix library. The finite Kraus representation theorem (milestone 5) requires Choi-matrix machinery that is not, to the knowledge of this proposal, available in Mathlib in this form; it is reusable well beyond this mission.

Difficulty

The goal and milestones 1-3 and 8 are finite-dimensional trace manipulations; the care needed is in keeping the order of products right, using the adjoint of the commutation relation, and summing over indices in the correct order. Milestone 7 needs an explicit entangled witness on C2⊗Cn\mathbb C^2\otimes\mathbb C^nC2⊗Cn. Milestone 5 is the substantial one: the obvious approach of writing T(ρ)T(\rho)T(ρ) in a basis does not produce Kraus operators directly; a positivity argument (via the Choi matrix of TTT and its spectral decomposition) is needed.

Formalization scope

All Hilbert spaces are finite dimensional: matrices are indexed by arbitrary finite types with complex entries. Outcome sets and discarded-subsystem label sets are finite types; Alice's and Bob's label sets may depend on the outcome. Kraus matrices for the single-intervention milestones may be rectangular (e×de\times de×d), reflecting that the system after the intervention may differ from the original one; in the no-communication theorem both observers' matrices are square on a common space. The order on C\mathbb CC used for "nonnegative probability" is the standard partial order (z≥0z\ge 0z≥0 iff zzz is real and nonnegative). Complete positivity quantifies over ancillas Cn\mathbb C^nCn for every n∈Nn\in\mathbb Nn∈N, with T⊗1T\otimes\mathbb 1T⊗1 acting blockwise. Time reversal is encoded by the linear map ρ↦ρT\rho\mapsto\rho^{T}ρ↦ρT; on Hermitian matrices this coincides with complex conjugation, and only the linear extension gives a meaningful complete-positivity statement.

The goal keeps the hypotheses of the source setting (both POVMs complete, ρ\rhoρ a density matrix); none of them can be dropped silently by a degenerate encoding, and the conclusion is an identity of complex numbers, so no hypothesis is vacuous: for instance, single-outcome trivial interventions with A=B=1A = B = \mathbb 1A=B=1 satisfy all of them.

Note: the sentence on p. 100 of the source claiming that commuting POVM elements are necessarily orthogonal projections is not included; as stated it fails (e.g. E1=E2=121E_1 = E_2 = \tfrac12\mathbb 1E1​=E2​=21​1).

Selected references

  • A. Peres and D. R. Terno, Quantum information and relativity theory, Rev. Mod. Phys. 76, 93-123 (2004). https://doi.org/10.1103/RevModPhys.76.93
  • K. Kraus, States, Effects, and Operations, Lecture Notes in Physics 190, Springer (1983). https://doi.org/10.1007/3-540-12732-1
  • M.-D. Choi, Completely positive linear maps on complex matrices, Linear Algebra Appl. 10, 285-290 (1975). https://doi.org/10.1016/0024-3795(75)90075-0
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