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Classical channel N(y∣x)N(y|x)N(y∣x) and its action NpNpNp, NqNqNq (Corollary 10.7.2)

Definition
WildeQIT_Channel

by aadarwal · Sep 7, 2026 · Mathlib 0df444a (Lean v4.33.1)

classical-informationentropyinformation-theorywilde-qit

A classical channel from an input alphabet X\mathcal{X}X to an output alphabet Y\mathcal{Y}Y is a conditional probability distribution N(y∣x)N(y|x)N(y∣x): for every input letter xxx, a probability distribution over output letters yyy. It acts on a probability distribution ppp on X\mathcal{X}X by

(Np)(y)  ≡  ∑xN(y∣x) p(x),(Np)(y) \;\equiv\; \sum_x N(y|x)\,p(x),(Np)(y)≡x∑​N(y∣x)p(x),

which is again a probability distribution, and on an arbitrary vector q:X→Rq:\mathcal{X}\to\mathbb{R}q:X→R by the same formula (Nq)(y)=∑xN(y∣x) q(x)(Nq)(y)=\sum_x N(y|x)\,q(x)(Nq)(y)=∑x​N(y∣x)q(x).

Channels are the maps under which the relative entropy is monotone (Corollary 10.7.2, Theorem 10.8.4).

Formalization Note. WildeQIT.Channel α β abbreviates α → WildeQIT.FinDist β, so (N x).prob y is N(y∣x)N(y|x)N(y∣x); N.apply p : FinDist β is NpNpNp and N.applyFun q : β → ℝ is NqNqNq for a plain real vector q. The two agree on p.prob.

Definition code
import Definitions.Def_WildeQIT_FinDist

/-!
Wilde, *Quantum Information Theory* (2nd ed.), Corollary 10.7.2 (Monotonicity of relative
entropy): a classical channel is a conditional probability distribution `N(y|x)`; it acts on a
probability distribution `p` by `(Np)(y) = ∑_x N(y|x) p(x)` and on a vector `q` by
`(Nq)(y) = ∑_x N(y|x) q(x)`.
-/

namespace WildeQIT

/-- A classical channel from `α` to `β`: for every input letter `x`, a probability distribution
`N x` over output letters, so that `(N x).prob y` is `N(y|x)`. -/
abbrev Channel (α β : Type) [Fintype β] := α → FinDist β

namespace Channel

variable {α β : Type} [Fintype α] [Fintype β]

/-- The output distribution `Np`, `(Np)(y) = ∑_x N(y|x) p(x)`. -/
noncomputable def apply (N : Channel α β) (p : FinDist α) : FinDist β where
  prob y := ∑ x, (N x).prob y * p.prob x
  nonneg y := Finset.sum_nonneg fun x _ => mul_nonneg ((N x).nonneg y) (p.nonneg x)
  sum_eq_one := by
    rw [Finset.sum_comm]
    simp_rw [← Finset.sum_mul, (N _).sum_eq_one, one_mul]
    exact p.sum_eq_one

/-- The action on an arbitrary vector `q : α → ℝ`: `(Nq)(y) = ∑_x N(y|x) q(x)`. -/
noncomputable def applyFun (N : Channel α β) (q : α → ℝ) : β → ℝ :=
  fun y => ∑ x, (N x).prob y * q x

@[simp] theorem apply_prob (N : Channel α β) (p : FinDist α) (y : β) :
    (N.apply p).prob y = ∑ x, (N x).prob y * p.prob x := rfl

@[simp] theorem applyFun_apply (N : Channel α β) (q : α → ℝ) (y : β) :
    N.applyFun q y = ∑ x, (N x).prob y * q x := rfl

end Channel

end WildeQIT
Source
Wilde, Quantum Information Theory 2nd ed. (Cambridge 2017; arXiv:1106.1445v8), Corollary 10.7.2, §Data-Processing Inequality, LaTeX label cor-cie:mono-rel-ent (roster-items.csv line 17041); the notions 'conditional probability distribution N(y|x) (classical channel)', Np and Nq used in its statement.

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