Theorem 8: adjoint logarithm characterization of equality
OpenQAlgorithms.Papers.Independent.Intakethird100.P51.dataProcessingEqualityLet be a finite CPTP map and let be normalized input states. Define on their common output support and extend by zero to its orthogonal complement. Let be the actual Hilbert–Schmidt adjoint. Then
This is the adjoint-logarithm characterization of equality, preserving all finite channels and using compressed output calculus.
import Definitions.Def_QAlgorithms_Papers_Independent_Intakethird100_P51
noncomputable section
open scoped Matrix ComplexOrder ComplexConjugate Matrix.Norms.L2Operator
open MeasureTheory
namespace QAlgorithms.Papers.Independent.Intakethird100.P51
attribute [local instance] Classical.propDecidable Classical.decEq
-- Exact interfaces from the frozen accepted library; no local redeclarations.
open QAlgorithms.Papers.Independent.Information100.P63 (Mat)
open QAlgorithms.Papers.Independent.Information100.P56 (entropy holevo IsLaw)
open QAlgorithms.Papers.Independent.Information100.P27 (average)
open QAlgorithms.Papers.Independent.Dependency50.P21 (relativeEntropy marginalAB channelAdjoint)
open QAlgorithms.Papers.Independent.Dependency50.P35 (marginalAC)
open QAlgorithms.Papers.Independent.Gap48.P19 (shannonNats)
open QAlgorithms.Papers.Independent.Gap48.P33 (measurementInformation)
section Statements
variable {A B C D J K O : Type}
variable [Fintype A] [Fintype B] [Fintype C] [Fintype D] [Fintype J] [Fintype K] [Fintype O]
variable [DecidableEq A] [DecidableEq B] [DecidableEq C] [DecidableEq D]
variable [DecidableEq J] [DecidableEq K] [DecidableEq O]
/-- Theorem 8, equation (40), PDF pp.15–17: actual adjoint and supported output
logarithms. For faithful inputs the two outputs have common support supp(Phi I),
so the cfc log difference is the unique zero extension from this support. -/
theorem dataProcessingEquality [Nonempty A] [Nonempty B] (Phi : Superop A B) (hPhi : IsChannel Phi) (rho gamma : Mat A)
(hr : FaithfulDensity rho) (hg : FaithfulDensity gamma) :
(relativeEntropy (Phi rho) (Phi gamma)).toReal = (relativeEntropy rho gamma).toReal ↔
logMatrix rho - logMatrix gamma =
channelAdjoint Phi (logMatrix (Phi rho) - logMatrix (Phi gamma)) := by sorry
end Statements
end QAlgorithms.Papers.Independent.Intakethird100.P51
end
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What the Lean code literally says, in plain math · gpt-6.1-sol
For finite nonempty index types , with decidable equality as used in the matrix definitions, let have a finite Kraus representation satisfying , and let be positive definite matrices of trace one on . Then
The logarithms are continuous-functional-calculus logarithms using , so they are defined even when an output is singular. For a matrix on , the map on the right is explicitly
where is the matrix unit on . The extended-real equals , with , if , and equals otherwise; denotes its real conversion, which sends to zero.
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.