Observation 1: abort factorization
OpenQAlgorithms.Papers.Independent.Intakethird100.P22.abort_factorizationLet be a normalized classical-device law with a common finite nonempty key alphabet and finite nonempty transcript, Eve-output and Eve-input alphabets. The marginal is independent of . The ideal pass law replaces the keys by a uniform perfectly correlated pair, independent of the actual marginal. The intermediate law copies Alice's key into Bob's register and preserves . Let be the abort probability, with an identical normalized abort branch in all three systems. Write for the operational NS distance, and for the pass-weighted secrecy, disagreement and security errors, respectively. Thus , , and .
Let and . Then
A common abort branch contributes no distinguishing advantage.
import Definitions.Def_QAlgorithms_Papers_Independent_Intakethird100_P22
noncomputable section
open scoped BigOperators
namespace QAlgorithms.Papers.Independent.Intakethird100.P22
attribute [local instance] Classical.propDecidable Classical.decEq
open QAlgorithms.Papers.Independent.Advance50.P47
(Box IsCorrelation IsNoSignaling HasLocalModel correlationMixture prBox)
open QAlgorithms.Papers.Independent.Information100.P56 (IsLaw)
open QAlgorithms.Papers.Independent.Information100.P27 (shannon)
open QAlgorithms.Papers.Independent.Gap48.P25 (classicalMI)
/-- PDF p.31, Observation 1, (90): equal abort branches factor out exactly. -/
theorem abort_factorization {K T E Z : Type}
[Fintype K] [Fintype T] [Fintype E] [Fintype Z]
[Nonempty K] [Nonempty T] [Nonempty E] [Nonempty Z] (r : KeyOutput K T E Z) :
cdDistance (withAbort r.abortProbability r.abort r.pass)
(withAbort r.abortProbability r.abort (idealCDKey r.pass)) = securityError r := by sorry
end QAlgorithms.Papers.Independent.Intakethird100.P22
end
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What the Lean code literally says, in plain math · gpt-6.1-sol
For all finite nonempty types , let have abort probability and pass and abort arrays . Each array is nonnegative, sums to over for every , and has independent of . Put
For arrays on , define
where and are arbitrary nonnegative real probability distributions on and , respectively. With , the assertion is
whose right side is the defined security error of . Both endpoints and are included.
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.