Monotonicity on average under PPT instruments
OpenQAlgorithms.Papers.Independent.Intakethird100.P83.pptAverageMonotonicityLet be a normalized state on finite nonzero local spaces . Let be a discrete PPT instrument with possibly different finite output spaces . Each branch is completely positive and trace nonincreasing, each map is completely positive, and the branch maps sum unconditionally on every operator to a trace-preserving channel. Set and, only when , set . The average below is unconditionally summable, with a zero summand when , and
This includes finite and countable outcome sets. LOCC instruments obey the same inequality because they are PPT instruments by inclusion (1).
import Definitions.Def_QAlgorithms_Papers_Independent_Intakethird100_P83
noncomputable section
open scoped Matrix ComplexConjugate ComplexOrder
open Filter
namespace QAlgorithms.Papers.Independent.Intakethird100.P83
open QAlgorithms.Papers.Independent.Support50.P11 (Loewner)
open QAlgorithms.Papers.Independent.Dependency50.P32 (partialTranspose)
open QAlgorithms.Papers.Independent.Information100.P34 (supportProjector)
variable {A B C D : Type*}
variable [Fintype A] [Fintype B] [Fintype C] [Fintype D]
variable [DecidableEq A] [DecidableEq B] [DecidableEq C] [DecidableEq D]
/-- Theorem 3, PDF p.3, expanding (6) on p.2. A PPT instrument includes
all LOCC instruments by inclusion (1). The outcome sum is unconditional,
including zero-probability branches, and output dimensions may differ from input. -/
theorem pptAverageMonotonicity [Nonempty A] [Nonempty B] [Nonempty C] [Nonempty D]
{O : Type*} (N : O → Matrix (A × B) (A × B) ℂ → Matrix (C × D) (C × D) ℂ)
(hN : PPTInstrument N) (rho : Matrix (A × B) (A × B) ℂ)
(hrho : IsDensityMatrix rho) :
0 ≤ entanglementW rho ∧
∃ s : ℝ, HasSum (fun i => branchContribution (N i) rho) s ∧ s ≤ entanglementW rho := by sorry
end QAlgorithms.Papers.Independent.Intakethird100.P83
end
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What the Lean code literally says, in plain math · gpt-6.1-sol
Let be finite nonempty types with decidable equality, let be any type, and let , for , be functions from complex matrices on to complex matrices on . On either bipartite space write . Assume that, for every , both and the function are represented by complex-linear maps that are completely positive, meaning their identity amplification by every finite reference dimension sends positive semidefinite matrices to positive semidefinite matrices, including reference dimension ; also assume for every . Assume further that there is a function represented by finitely many Kraus matrices , so that and , such that for every matrix , with convergence understood as the net of sums over finite subsets of . Let have trace , and, for any bipartite matrix , define
using the Euclidean operator norm and matrices on the same space as . Set , and define if , and otherwise. The theorem asserts and the existence of a real such that , again as a net of finite-subset sums, and . No finiteness or countability assumption is imposed on .
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.