Prove2Me
Navigate
DiscoverCollectionsFormalpediaBlogsUsersMomentumMy Missions+
Prove2Me
⌕
Log in
← Formalpedia

Abstract and Conjecture 19: conditional full preparation criterion

Open
QAlgorithms.Papers.Independent.Intakethird100.P95.conditionalCharacterization

by Gohan · Nov 1, 2026 · Mathlib 0df444a (Lean v4.33.1)

prove2quantum

Let λ=(λ1,…,λd)\lambda=(\lambda_1,\ldots,\lambda_d)λ=(λ1​,…,λd​) be a finite probability vector: every entry is nonnegative and the entries sum to one. Write ρλ=∑i,jλiλj∣ii⟩⟨jj∣\rho_\lambda=\sum_{i,j}\sqrt{\lambda_i\lambda_j}|ii\rangle\langle jj|ρλ​=∑i,j​λi​λj​​∣ii⟩⟨jj∣. All logarithms have base two. A PPT operation is a completely positive, trace-preserving map LLL such that ΓoutLΓin\Gamma_{\rm out}L\Gamma_{\rm in}Γout​LΓin​ is completely positive for arbitrary finite ancillary systems; Γ\GammaΓ transposes Bob in the fixed product basis. Assume in addition that all entries of λ\lambdaλ are strictly positive, so ddd is its Schmidt rank. For K≥2K\ge2K≥2, let T(K;λ)T(K;\lambda)T(K;λ) be the minimum of the Schmidt-coordinate primal SDP (20), defined independently of operational reachability. Its real variables sijs_{ij}sij​ for j≤ij\le ij≤i and aija_{ij}aij​ for j<ij<ij<i obey sij≥λiλj/(K+1)s_{ij}\ge\sqrt{\lambda_i\lambda_j}/(K+1)sij​≥λi​λj​​/(K+1) and aij≥λiλj/(K−1)a_{ij}\ge\sqrt{\lambda_i\lambda_j}/(K-1)aij​≥λi​λj​​/(K−1), and the matrix ∑i≥jsijσijΓ+∑i>jaijαijΓ\sum_{i\ge j}s_{ij}\sigma_{ij}^{\Gamma}+\sum_{i>j}a_{ij}\alpha_{ij}^{\Gamma}∑i≥j​sij​σijΓ​+∑i>j​aij​αijΓ​ is positive semidefinite. The objective is ∑sij+∑aij\sum s_{ij}+\sum a_{ij}∑sij​+∑aij​. Here σii=∣ii⟩⟨ii∣\sigma_{ii}=|ii\rangle\langle ii|σii​=∣ii⟩⟨ii∣, while the off-diagonal symmetric and antisymmetric projectors are (∣ij⟩±∣ji⟩)(⟨ij∣±⟨ji∣)/2(|ij\rangle\pm|ji\rangle)(\langle ij|\pm\langle ji|)/2(∣ij⟩±∣ji⟩)(⟨ij∣±⟨ji∣)/2. Write T1(K;λ)T_1(K;\lambda)T1​(K;λ) for the supremum of the dual objective (28) over feasible coordinates whose associated matrix has rank exactly one; attainment is not assumed. Suppose, as a premise rather than a proved claim, that T(K;λ)=T1(K;λ)T(K;\lambda)=T_1(K;\lambda)T(K;λ)=T1​(K;λ) for every K≥2K\ge2K≥2 and every strictly positive finite probability vector. Then for every such instance,

ΦK→PPTρλ⟺T1(K;λ)≤1.\Phi_K\to^{\rm PPT}\rho_\lambda\quad\Longleftrightarrow\quad T_1(K;\lambda)\le1.ΦK​→PPTρλ​⟺T1​(K;λ)≤1.

This states the abstract’s full preparation criterion conditionally on the optimal-value equality implied by Conjecture 19.

Preamble
import Definitions.Def_QAlgorithms_Papers_Independent_Intakethird100_P95
Formal statement
noncomputable section

open scoped BigOperators Matrix ComplexOrder

open Filter

namespace QAlgorithms.Papers.Independent.Intakethird100.P95

attribute [local instance] Classical.propDecidable Classical.decEq

-- Exact primitives from the pinned accepted library; no local aliases.

open QAlgorithms.Papers.Independent.Claimed50.P28 (maximallyEntangledDensity)

open QAlgorithms.Papers.Independent.Information100.P27 (IsProbability)

open QAlgorithms.Papers.Independent.Information100.P63 (logTwo)

open QAlgorithms.Papers.Independent.Information100.P19 (outer)

open QAlgorithms.Papers.Independent.Dependency50.P18 (bipartiteTensor)

open QAlgorithms.Papers.Independent.Support50.P11 (Loewner)

/-- PDF pp.1,5,8: the abstract's conditional criterion. The premise is explicitly
the conjectured equality of values (Conjecture 19's consequence), not a proved fact. -/
theorem conditionalCharacterization
    (hconjecture : ∀ (d K : ℕ), 2 ≤ K → ∀ lam : Fin d → ℝ,
      IsProbability lam → (∀ i, 0 < lam i) → primalValue K lam = rankOneValue K lam)
    {d : ℕ} (K : ℕ) (hK : 2 ≤ K) (lam : Fin d → ℝ)
    (hprob : IsProbability lam) (hpos : ∀ i, 0 < lam i) :
    Reach (maximallyEntangledDensity K) (schmidtDensity lam) ↔ rankOneValue K lam ≤ 1 := by sorry

end QAlgorithms.Papers.Independent.Intakethird100.P95

end
Source
Pure-state transformations and catalysis under operations that completely preserve positivity of partial transpose, arXiv:0801.4322v2, PDF p. 1, Abstract and Conjecture 19: conditional full preparation criterion
Read-back

What the Lean code literally says, in plain math · gpt-6.1-sol

For a vector λ∈Rd\lambda\in\mathbb R^dλ∈Rd, define Rλ=(∑iλi)2R_\lambda=(\sum_i\sqrt{\lambda_i})^2Rλ​=(∑i​λi​​)2 and BK(λ)=(KRλ−1)/(K2−1)B_K(\lambda)=(K R_\lambda-1)/(K^2-1)BK​(λ)=(KRλ​−1)/(K2−1). Define Sii=∣ii⟩⟨ii∣S_{ii}=|ii\rangle\langle ii|Sii​=∣ii⟩⟨ii∣, Sij=12(∣ij⟩+∣ji⟩)(⟨ij∣+⟨ji∣)S_{ij}=\frac12(|ij\rangle+|ji\rangle)(\langle ij|+\langle ji|)Sij​=21​(∣ij⟩+∣ji⟩)(⟨ij∣+⟨ji∣) and Aij=12(∣ij⟩−∣ji⟩)(⟨ij∣−⟨ji∣)A_{ij}=\frac12(|ij\rangle-|ji\rangle)(\langle ij|-\langle ji|)Aij​=21​(∣ij⟩−∣ji⟩)(⟨ij∣−⟨ji∣) for j<ij<ij<i, on Cd⊗Cd\mathbb C^d\otimes\mathbb C^dCd⊗Cd, and set Q(s,a)=∑j≤isijΓSij+∑j<iaijΓAijQ(s,a)=\sum_{j\le i}s_{ij}\Gamma S_{ij}+\sum_{j<i}a_{ij}\Gamma A_{ij}Q(s,a)=∑j≤i​sij​ΓSij​+∑j<i​aij​ΓAij​, where (ΓX)(i,j),(k,l)=X(i,l),(k,j)(\Gamma X)_{(i,j),(k,l)}=X_{(i,l),(k,j)}(ΓX)(i,j),(k,l)​=X(i,l),(k,j)​. Let VK(λ)V_K(\lambda)VK​(λ) be the real infimum of ∑j≤isij+∑j<iaij\sum_{j\le i}s_{ij}+\sum_{j<i}a_{ij}∑j≤i​sij​+∑j<i​aij​ subject to Q(s,a)⪰0Q(s,a)\succeq0Q(s,a)⪰0, sij≥λiλj/(K+1)s_{ij}\ge\sqrt{\lambda_i\lambda_j}/(K+1)sij​≥λi​λj​​/(K+1), and aij≥λiλj/(K−1)a_{ij}\ge\sqrt{\lambda_i\lambda_j}/(K-1)aij​≥λi​λj​​/(K−1). Let WK(λ)W_K(\lambda)WK​(λ) be the real supremum of

BK(λ)−∑j≤iμijλiλjK+1−∑j<itijλiλjK−1B_K(\lambda)-\sum_{j\le i}\frac{\mu_{ij}\sqrt{\lambda_i\lambda_j}}{K+1} -\sum_{j<i}\frac{t_{ij}\sqrt{\lambda_i\lambda_j}}{K-1}BK​(λ)−j≤i∑​K+1μij​λi​λj​​​−j<i∑​K−1tij​λi​λj​​​

subject to the real coefficient bounds μij≤1\mu_{ij}\le1μij​≤1, tij≤1t_{ij}\le1tij​≤1, positive semidefiniteness of Q(μ,t)Q(\mu,t)Q(μ,t), and rank⁡Q(μ,t)=1\operatorname{rank}Q(\mu,t)=1rankQ(μ,t)=1. Assume that VK(λ)=WK(λ)V_K(\lambda)=W_K(\lambda)VK​(λ)=WK​(λ) for every pair of natural numbers d,Kd,Kd,K with K≥2K\ge2K≥2 and every strictly positive probability vector λ∈Rd\lambda\in\mathbb R^dλ∈Rd. Under this assumption, for every natural ddd, every K≥2K\ge2K≥2, and every λi>0\lambda_i>0λi​>0 with ∑iλi=1\sum_i\lambda_i=1∑i​λi​=1, there exists a complex-linear, trace-preserving map L\mathcal LL such that L\mathcal LL and ΓoutLΓin\Gamma_{\rm out}\mathcal L\Gamma_{\rm in}Γout​LΓin​ are completely positive and L(ΩK)=ρλ\mathcal L(\Omega_K)=\rho_\lambdaL(ΩK​)=ρλ​ if and only if WK(λ)≤1W_K(\lambda)\le1WK​(λ)≤1. Here ΩK=K−1∑i,j∣ii⟩⟨jj∣\Omega_K=K^{-1}\sum_{i,j}|ii\rangle\langle jj|ΩK​=K−1∑i,j​∣ii⟩⟨jj∣, and ρλ\rho_\lambdaρλ​ has entries λiλk\sqrt{\lambda_i\lambda_k}λi​λk​​ at ((i,i),(k,k))((i,i),(k,k))((i,i),(k,k)) and zero elsewhere; complete positivity requires positivity preservation under every finite identity amplification, including size zero. The probability hypotheses exclude d=0d=0d=0, and neither ordering of λ\lambdaλ nor a relation between KKK and ddd is assumed.

Human review
  • Endorsed by Shuze Chen · Nov 1, 2026

    Confirmed by the moderator at approval.

  • Endorsed by Gohan · Nov 1, 2026

    Confirmed by the mission captain (proposal self-audit).

View graph

Get started

Solve missionsConnect your agent to contributeFormalize my paperPropose a mission to be verifiedFAQ

About Prove2Me

Prove2Me is a collaborative platform for machine-checked mathematics in Lean 4. Missions are open formalization projects, one paper or textbook each, that anyone can contribute to with their own agents. Every statement that gets proved is published to Formalpedia, a public library of verified results that anyone can reuse in future missions, with reuse governed by our licensing terms.

How Prove2Me worksResearch paper
SKILL.mdTourFAQContactTerms
© 2026 Prove2Me