Exact Measurement-Dependence Cost across the Noisy GHZ–Mermin Threshold

We determine the exact measurement-dependence cost of faithful local deterministic models reproducing the full outcome table on the even-parity GHZ–Mermin setting set for Pauli X/Y measurements. For every integer n ≥ 3 and isotropic visibility 0 ≤ v ≤ 1, let R = 2^floor((n−1)/2). The minimum worst-pair total-variation distance between setting-conditioned hidden-variable distributions is F_n(v) = max{0, (R²v−R)/[2(R²−1)]}. Measurement-independent models suffice precisely up to v = 1/R; above this threshold, the minimum cost increases linearly, reaching R/[2(R+1)] at perfect visibility. Quadratic Walsh spectra yield explicit optimal ensembles, a faithful lift restores all vanishing proper-subset correlators without changing the cost, and a nondegenerate R²-setting subsystem supplies a matching lower bound. Relative to version 1, this version extends the perfect-visibility result to the complete isotropic visibility curve and expands the derivation details and literature discussion. The result concerns this specified full-table family and does not assert an exact cost for arbitrary noise.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-13
DOI
https://doi.org/10.5281/zenodo.22089933
Primary Topic
Markov Chains and Monte Carlo Methods
Type
preprint

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preprint

Exact Measurement-Dependence Cost across the Noisy GHZ–Mermin Threshold

Kun Chen, Zhiyuan Yao, Qihang Wang
Zenodo (CERN European Organization for Nuclear Research)
Markov Chains and Monte Carlo Methods
preprint

Exact Measurement-Dependence Cost across the Noisy GHZ–Mermin Threshold

Kun Chen, Zhiyuan Yao, Qihang Wang
preprint en

Abstract

We determine the exact measurement-dependence cost of faithful local deterministic models reproducing the full outcome table on the even-parity GHZ–Mermin setting set for Pauli X/Y measurements. For every integer n ≥ 3 and isotropic visibility 0 ≤ v ≤ 1, let R = 2^floor((n−1)/2). The minimum worst-pair total-variation distance between setting-conditioned hidden-variable distributions is F_n(v) = max{0, (R²v−R)/[2(R²−1)]}. Measurement-independent models suffice precisely up to v = 1/R; above this threshold, the minimum cost increases linearly, reaching R/[2(R+1)] at perfect visibility. Quadratic Walsh spectra yield explicit optimal ensembles, a faithful lift restores all vanishing proper-subset correlators without changing the cost, and a nondegenerate R²-setting subsystem supplies a matching lower bound. Relative to version 1, this version extends the perfect-visibility result to the complete isotropic visibility curve and expands the derivation details and literature discussion. The result concerns this specified full-table family and does not assert an exact cost for arbitrary noise.

Zenodo (CERN European Organization for Nuclear Research)
Peking University (CN), Institute of Theoretical Physics (CN), Lanzhou University (CN)
National Natural Science Foundation of China, Chinese Academy of Sciences
Markov Chains and Monte Carlo Methods
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