Mixed-state tightness and failure of the support-projector choice
OpenQAlgorithms.Papers.Independent.Intakethird100.P83.deterministicMixedTightnessLet be the equal mixture of the three normalized qutrit vectors , , and . Let be its support projector. With the one-copy exact positive-real-target PPT rate and its regularization, one has
Moreover, , so the projector lower bound is . This genuinely mixed example makes the first three comparison inequalities equalities. LOCC achievability remains open in the source.
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]
/-- Mixed-state coincidence and failure of the R=P choice, PDF p.5,
with values from Proposition 5 on p.4. No LOCC achievability is asserted. -/
theorem deterministicMixedTightness :
oneCopyExact (rhoAlpha (1/2)) = Real.logb 2 (3/2) ∧
exactRegularized (rhoAlpha (1/2)) = Real.logb 2 (3/2) ∧
entanglementGamma (rhoAlpha (1/2)) = Real.logb 2 (3/2) ∧
entanglementW (rhoAlpha (1/2)) = Real.logb 2 (3/2) ∧
entanglementN (rhoAlpha (1/2)) = Real.logb 2 (5/3) ∧
Real.logb 2 (3/2) < Real.logb 2 (5/3) ∧
complexSpectralNorm (partialTranspose (supportProjector (rhoAlpha (1/2)))) = 1 ∧
-Real.logb 2 (complexSpectralNorm (partialTranspose (supportProjector (rhoAlpha (1/2))))) = 0 ∧
0 < oneCopyExact (rhoAlpha (1/2)) := by sorry
end QAlgorithms.Papers.Independent.Intakethird100.P83
end
Read-back
What the Lean code literally says, in plain math · gpt-6.1-sol
On , with standard basis , define , and let be the orthogonal projector onto its range. For a bipartite matrix , define , , and let denote the Euclidean operator norm. Define as the real supremum of over satisfying , , and , where denotes positive semidefinite difference. Put , , and , without restricting to be nonnegative; tensor powers are defined entrywise by products, with zeroth power the scalar . Set , where is Hermitian, , and , and set . All these suprema are real-valued. Then
and additionally
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.