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MIPRE.Background.LIDT.Co.Test.MainTheorem.ProjectiveConsistency.Evaluation

Projective consistency evaluation #

The data-processing lemmas which turn polynomial-level projective consistency into pointwise consistency after evaluation at a sampled point: the counterpart of MIPRE/Background/LIDT/MIPStarRE/LDT/Test/MainTheorem/ProjectiveConsistency/Evaluation.lean in the port of planning/c6b-plan.md (milestone M13, section "Port conventions").

The same-space statements consRel_constPolynomialEvaluation and projectiveEvaluationConsistency_ofFullPolynomialConsistency take the symmetric model S : SymModel 𝔓 K in place of the vendored state on ΞΉ Γ— ΞΉ, and go through Co Preliminaries.approxToSimeq and Preliminaries.consRelDataProcessing_questionDependent.

The two _heterogeneous theorems are two-space. Their vendored state is on ΞΉA Γ— ΞΉB, with Alice's polynomial measurement on ΞΉA and Bob's on ΞΉB, and their callers are the two-space tail of the main theorem on the strategy's own model. So they are stated over a bipartite model M : MIPRE.BipartiteModel π’ž π’œ ℬ, with A, Q_A in π’œ and B, Q_B in ℬ: consistency is M2's two-space defect, bipartiteConsError M π’Ÿ A B ≀ Ξ΄ for the vendored ConsRel ψ π’Ÿ A B Ξ΄, and the hypothesis of the projective form is the state-dependent distance of the placements TwoSpace.leftPlacedSubMeas M Q_A, TwoSpace.rightPlacedSubMeas M Q_B on the vector state TwoSpace.vecState M hψ (Co/Test/MainTheorem/TwoSpace.lean). This departs from the narrowing rule of "Same-space and bipartite quantities" in the way M8's heterogeneous orthonormalizations did (Co/MakingMeasurementsProjective/Orthonormalization.lean): narrowed to one carrier, they would repeat their same-space siblings and serve no caller. The proofs are the vendored ones, through TwoSpace.consRelDataProcessing_questionDependent and TwoSpace.approxToSimeq_heterogeneous.

Not ported #

Every declaration of the vendored file has a counterpart here.

Over a symmetric model #

A constant full-polynomial consistency statement postprocesses to pointwise polynomial evaluation with the same error.

This is the data-processing move used after paper line 156: once Q^A_g βŠ— I ≃ I βŠ— Q^B_g is available over the single polynomial question, evaluating both polynomial outcomes at a point u preserves consistency over the uniform point distribution.

Two-space form of consRel_constPolynomialEvaluation: the same data-processing argument when Alice's polynomial measurement lies in the first player's algebra π’œ of a bipartite model M and Bob's in the second player's algebra ℬ, with M2's two-space defect for the vendored ConsRel.

Turn a line-156 projective approximation into the evaluated consistency used in the final point-consistency triangles.

The proof first applies the projective converse of prop:simeq-to-approx at the polynomial level, then uses question-dependent data processing to evaluate both projective polynomial measurements at each point.

Over a bipartite model #

theorem MIPRE.LIDT.Co.Test.projectiveEvaluationConsistency_ofFullPolynomialConsistency_heterogeneous {params : MIPStarRE.LDT.Parameters} [MIPStarRE.LDT.FieldModel params.q] {π’ž : Type u_1} {π’œ : Type u_2} {ℬ : Type u_3} [Ring π’ž] [StarRing π’ž] [Algebra β„‚ π’ž] [Ring π’œ] [StarRing π’œ] [Algebra β„‚ π’œ] [PartialOrder π’œ] [StarOrderedRing π’œ] [Ring ℬ] [StarRing ℬ] [Algebra β„‚ ℬ] [PartialOrder ℬ] [StarOrderedRing ℬ] {M : BipartiteModel π’ž π’œ ℬ} (hψ : β€–M.Οˆβ€– = 1) (Q_A : ProjMeas (MIPStarRE.LDT.Polynomial params) π’œ) (Q_B : ProjMeas (MIPStarRE.LDT.Polynomial params) ℬ) {΢₃ : ℝ} (hline : (TwoSpace.vecState M hψ).SDDRel (MIPStarRE.LDT.uniformDistribution Unit) (constSubMeasFamily (TwoSpace.leftPlacedSubMeas M Q_A.toSubMeas)) (constSubMeasFamily (TwoSpace.rightPlacedSubMeas M Q_B.toSubMeas)) ΢₃) :

Two-space form of projectiveEvaluationConsistency_ofFullPolynomialConsistency: for projective polynomial measurements Q_A of π’œ and Q_B of ℬ whose placements are at state-dependent distance ΢₃ on the unit state of M, the evaluated families are two-space consistent at ΢₃ / 2.

It first applies the two-space projective converse (TwoSpace.approxToSimeq_heterogeneous) to the placed state-dependent-distance relation, and then evaluates the polynomial outcomes at the sampled point.