Classical channel and its action , (Corollary 10.7.2)
DefinitionWildeQIT_Channelclassical-informationentropyinformation-theorywilde-qit
A classical channel from an input alphabet to an output alphabet is a conditional probability distribution : for every input letter , a probability distribution over output letters . It acts on a probability distribution on by
which is again a probability distribution, and on an arbitrary vector by the same formula .
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.apply p : FinDist β is and N.applyFun q : β → ℝ is 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.