Concatenating a source identity with a relative communication identity
OpenQAlgorithms.Papers.Independent.Intakethird100.P64.sourceIdentityConcatenationLet be a finite nonzero source register with density matrix , purified by a unit vector . The reference is inaccessible to Alice and Bob. The registers are identified copies of , so the two occurrences of denote the same density under relabeling. Then
For every positive error and rate slack, all sufficiently large positive have local encoding and decoding maps using supplied identity-channel calls on the Source-supplied systems. For every positive input-promise tolerance , the actual encoded input marginal is within of . The code succeeds jointly with for every physical implementation of the relative identity satisfying its joint error guarantee on inputs with that near-IID marginal. The local maps are chosen before that implementation is supplied. No additional communication or shared state is supplied.
This is direct routing of the specified source, rather than a general transitivity principle for arbitrary resources.
import Definitions.Def_QAlgorithms_Papers_Independent_Intakethird100_P64
noncomputable section
namespace QAlgorithms.Papers.Independent.Intakethird100.P64
open QAlgorithms
open QAlgorithms.Papers.Independent.Gap48.P20
(Copies redistributionMarginal subsystemEntropy forwardApply)
open QAlgorithms.Papers.Independent.Dependency50.P48 (densityPower)
open scoped Matrix ComplexConjugate
attribute [local instance] Classical.propDecidable Classical.decEq
/-- PDF p.3, unnumbered display and Fig.3: concatenate the Source-to-Alice
identity with the relative Alice-to-Bob identity for the same rho. The actual
source purification and the encoder's marginal are part of the operational test. -/
theorem sourceIdentityConcatenation {R S : Type} [Fintype R] [Fintype S]
[Nonempty R] [Nonempty S] (phi : EuclideanSpace ℂ (R × S))
(hphi : IsState phi) : SourceRoutingAchievable phi := by sorry
end QAlgorithms.Papers.Independent.Intakethird100.P64
end
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What the Lean code literally says, in plain math · gpt-6.1-sol
For nonempty types equipped with finite enumerations and every vector with , define
The theorem asserts that for every , there exists such that for every with , there exist channels on matrices indexed by satisfying and, for every ,
and the following statement for every channel on those matrices: if, for every positive integer and positive semidefinite trace-one matrix on ,
then
A channel here means a matrix function for some finite Kraus family with ; partial traces sum diagonal blocks, and tensoring with an identity applies to each reference-index block. The trace norm is . The channels are chosen independently of , so their displayed marginal difference must have norm zero.
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.