Example 7: lower-order Renyi entropies are not PPT monotones
OpenQAlgorithms.Papers.Independent.Intakethird100.P95.example7Let be a finite probability vector: every entry is nonnegative and the entries sum to one. Write . All logarithms have base two. A PPT operation is a completely positive, trace-preserving map such that is completely positive for arbitrary finite ancillary systems; transposes Bob in the fixed product basis. For a finite order , is the Rényi entropy, with the log support size, (using ), and otherwise. The separate infinity order is . Consider the specific spectrum
It has . There is an integer sequence with such that for every sufficiently large integer . For each real with ,
For every such there are positive and an integer for which this exact conversion holds and the input entropy is strictly less than the output entropy . Thus every order in that interval fails to be a PPT monotone.
import Definitions.Def_QAlgorithms_Papers_Independent_Intakethird100_P95
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 p.4, Example 7: integer n+o(n) resource copy counts, the exact asymptotic
conversion, and the strict inequality at EVERY order 0 ≤ t < 1/2. The alpha/t
mismatch and the down-arrow on an ascending list are source typographical errors. -/
theorem example7 :
IsProbability exampleSpectrum ∧ schmidtRenyi (1 / 2) exampleSpectrum = logTwo 5 ∧
(∃ m : ℕ → ℕ, Tendsto (fun n => (m n : ℝ) / (n : ℝ)) atTop (nhds 1) ∧
∃ N : ℕ, ∀ n ≥ N, Reach (maximallyEntangledDensity (5 ^ m n)) (rhoPower exampleSpectrum n)) ∧
(∀ t : ℝ, 0 ≤ t → t < 1 / 2 → logTwo 5 < schmidtRenyi t exampleSpectrum) ∧
(∀ t : ℝ, 0 ≤ t → t < 1 / 2 →
∃ n m : ℕ, 0 < n ∧ Reach (maximallyEntangledDensity (5 ^ m)) (rhoPower exampleSpectrum n) ∧
logTwo ((5 ^ m : ℕ) : ℝ) < schmidtRenyi t (tensorSpectrum exampleSpectrum n)) := by sorry
end QAlgorithms.Papers.Independent.Intakethird100.P95
end
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What the Lean code literally says, in plain math · gpt-6.1-sol
Let , indexed by , and write . For a finite real vector , define , , and when , using real powers. Put , indexed by words , and define to have entry between and and zero elsewhere. For positive , put . Say that reaches when a complex-linear, trace-preserving, completely positive map takes exactly to and its conjugation by partial transposition on the second subsystem is also completely positive; complete positivity includes every finite-dimensional identity amplification. The declaration asserts that has nonnegative entries summing to one, , there exists a function with and a natural number such that reaches for every , and, for every real with , both
The last witnesses may depend on , and their is allowed to be zero; the sequence is not required to be positive. The quotient at uses division by zero defined as zero, and the tensor-power definitions include the empty product at .
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.