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arXiv 2610.11377quant-phcs.ITmath.IT

见证可能存在的真正多体非定域性的极小场景

Minimal Scenarios Witnessing Genuine Multipartite Nonlocality Possibilistically

Soumyadip Patra, Peter Bierhorst

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中文总结 AI 辅助

该研究探讨真正多体非定域性,利用 inflation 技术证明五体贝尔场景下存在稳健 LOSR-GMNL 游戏,构建的ε-最优量子策略可任意精度逼近概率1获胜策略。

中文摘要 AI 辅助

我们研究一种强形式的真正多体非定域性(GMNL),其中n体场景存在量子策略,可确定生成属于受限集合的结果,而由至多k<n个 parties 共享的非经典资源构建的每一种策略,都会以正概率不满足该判据。这推广了所谓的强语境性/非定域性或伪心灵感应,超越了排除局域模型的范畴。对于最简单的五体贝尔场景,每个 party 有两个二元结果测量设置,我们利用 inflation 技术证明,具有完美量子策略的游戏,在局域操作与共享随机性(LOSR)下,与由二体资源构建的模型存在稳健分离,确立该行为为 LOSR-GMNL。我们通过在不同资源模型和 GMNL 的替代定义下,研究三体或四体共享资源的可模拟性,为该结果提供更广泛背景。一个核心开放问题是,对于n≥3,n体量子资源能否实现与由(n-1)体资源构建的模型的此类分离。我们在此取得进展,构建了一类稳健 LOSR-GMNL 游戏,其具有ε-最优量子策略,在极限情况下可任意期望精度下,与真正的概率1获胜策略实验不可区分。

英文摘要

We study a strong form of genuine multipartite nonlocality (GMNL), where an $n$-partite scenario admits a quantum strategy generating outcomes belonging to a restricted set with certainty, whereas every strategy built from nonclassical resources shared among at most $k<n$ parties fails this criterion with positive probability. This generalizes what is referred to as strong contextuality/nonlocality, or pseudotelepathy, beyond ruling out local models. For the simplest five-party Bell scenario of two binary-outcome measurement settings per party, we use the inflation technique to demonstrate that a game with a perfect quantum strategy witnesses a robust separation from models built from bipartite resources under local operations and shared randomness (LOSR), establishing the behavior is LOSR-GMNL. We provide broader context for this result by investigating its simulability with resources shared among three or four parties under different resource models and alternative definitions of GMNL. A central open question is whether $n$-party quantum resources can achieve such a separation from models built from $(n-1)$-party resources, for $n\ge 3$. We make progress here by constructing a family of robustly LOSR-GMNL games with $ε$-optimal quantum strategies experimentally indistinguishable in the limit from true probability-one-winning strategies for any desired level of precision.

发表机构

  • LSU New Orleans(路易斯安那州立大学新奥尔良分校)

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