Theorem 2: biorthogonal mixtures preserve SSA equality and yield correlated nonproduct cases
OpenQAlgorithms.Papers.Independent.Intakethird100.P63.theorem2_biorthogonalSSAAll states below are positive trace-class density operators of trace one on completed complex Hilbert tensor products, represented in countable orthonormal coordinates. Finite dimensional systems are included. Entropies use natural logarithms and the convention . Every subsystem entropy occurring in an entropy difference is finite; this is the proof-supported finite-entropy reading of the source, and imposes no finite Hilbert dimension. A discrete ensemble may have finitely or countably many components: its weights are nonnegative and sum to one, and each component with is a normalized density operator. Every mixture below is the computed trace-norm convergent sum . All component conditions concern positive-weight labels only.
For a tripartite ensemble suppose that for distinct active labels , both and . If every active component satisfies , then
Moreover, if at least two labels have positive weight and , then
Here and all subscripts denote actual marginals. The result gives SSA equality beyond independent-third-factor states.
import Definitions.Def_QAlgorithms_Papers_Independent_Intakethird100_P63
noncomputable section
open scoped ENNReal BigOperators
namespace QAlgorithms.Papers.Independent.Intakethird100.P63
attribute [local instance] Classical.propDecidable Classical.decEq
-- Reuse the accepted nuclear trace-class, positive, trace-one predicate verbatim.
open QAlgorithms.Papers.Independent.Dependency50.P33 (IsDensity)
open QAlgorithms.Papers.Independent.Dependency50.P13 (finiteRelativeEntropy)
/-- Theorem 2, PDF pp.5,7: both SSA equality and positivity outside the aggregate
product exception (8). Finiteness of mixture marginals implies it for active components.
Zero labels do not count toward the condition of at least two terms. -/
theorem theorem2_biorthogonalSSA (e : DiscreteDensityEnsemble (Fin 3 → ℕ))
(hbio : Biorthogonal e {0} {1})
(hfinite : FiniteSubsystemEntropies (herbutMixture e))
(hssa : ∀ k, 0 < e.weight k →
clusterMutualInformation (e.component k) {0} {1} = clusterMutualInformation (e.component k) {0} {1, 2}) :
clusterMutualInformation (herbutMixture e) {0} {1} = clusterMutualInformation (herbutMixture e) {0} {1, 2} ∧
((∃ k l, k ≠ l ∧ 0 < e.weight k ∧ 0 < e.weight l) →
herbutMixture e ≠ productAcross (herbutMixture e) {0, 1} →
0 < clusterMutualInformation (herbutMixture e) {1} {2}) := by sorry
end QAlgorithms.Papers.Independent.Intakethird100.P63
end
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What the Lean code literally says, in plain math · gpt-6.1-sol
Let consist of nonnegative summable real weights , , with , and complex kernels on . For every with , require an entrywise expansion with square-summable vectors and , real nonnegative finite quadratic forms for every finite and arbitrary complex , and summable real diagonal of sum ; zero-weight components are unrestricted. For any kernel on , define . Define as the infimum in of over representations , with nonnegative summable weights of sum and square-summable vectors that are unit and pairwise orthogonal at distinct positive-weight indices; an empty infimum is . Put , with converted to , and . Assume that, for all distinct with positive weights and for each , the reduced kernels satisfy for every . Let , assume for every , including the empty set, and assume for every positive-weight . Then
In addition, if there exist two distinct positive-weight indices and is unequal to the kernel , then . All kernel series use total infinite sums; finite subsystem entropies are assumed for , with no such assumption on the individual components.
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.