智源社区 2024年07月16日
静5青年讲座 | Coherence in Property Testing
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本文探讨了量子与经典概率分布的性质测试问题。研究发现,即使量子状态的相干性也无法显著改善对分布支持大小的测试能力。文章进一步引入多证明者模型,发现经典性质测试仍然存在局限性,而量子子集状态证明却能显著提高测试效率,展现了量子与经典在性质测试中的分离现象。

🔍 经典概率分布的性质测试表明,要验证未知分布的支持大小需要Ω(n^1/2)样本量。即使是平坦分布,区分大小区别的难度仍然很高。

🌌 在量子设置中,量子状态的相干性允许更全局的描述概率分布。然而,研究表明相干性本身并不足以改善支持大小的可测试性。

🔐 文章提出了一种新的编码平坦分布的方法,即子集状态,并证明了在支持大小不过小或不过大的情况下,子集状态与哈尔随机状态不可区分。

🤖 在多证明者模型中,即使与多个AM证明者互动,经典性质测试仍无法有效估计支持大小,而量子子集状态证明却能显著提高测试效率。

🧪 文章的结果展示了在自然属性的性质测试中相干性的力量和局限性,并建立了在多个参数上指数级的量子-经典分离。

Property testing is a fundamental task both classically and quantumly. There is a wealth of results investigating property testing of classical probability distributions, where a tester is given samples of a distribution typically over a large domain, say  for large n. A central classical result is that to verify the support size of an unknown distribution requires  (Valiant&Valiant, STOC11) . In fact, to distinguish vastly different support sizes of flat distributions is hard: even given  samples, no tester can distinguish between flat distributions of support size  from  with probability better than  .

In the quantum setting, quantum states can be in a coherent superposition of many states of  , in particular, allowing a more global description of probability distributions. One can then ask how much coherence helps property testing. A natural way to encode a flat distribution is via subset states,   . We show that coherence alone is not enough to improve the testability of support size, (1) Even given  copies, no tester can distinguish between subset states of size  from  with probability better than  . In fact, we obtain a more general result showing that subset states are indistinguishable from Haar random states provided the support size is not too small nor too large. This also provides new constructions of pseudorandom (PRS) and pseudoentangled states. 

Given that standard property testing fails badly here, we enhance these models by allowing multiple provers. In particular, we show that  even interacting with multiple AM provers, classical property testing still fails, (2) Even given  samples from a classical distributions and interacting with  AM provers in  rounds, no classical tester can estimate the support size up to factors  with probability better than  . We obtain this classical lower bound via a curious reduction to fast mixing of high-dimensional expanders.

In contrast, we show that coherent subset state proofs suffice to improve testability exponentially, (3) With just polynomially many copies and subset state proofs, a tester can, with high probability, approximate the support size of a subset state of arbitrary size, or detect that the certificates are malicious. Our results show some of the power and limitations of coherence in property testing for a natural property and establish quantum-classical separations that are exponential in various parameters. 

Pei Wu obtained his PhD from UCLA. He is currently is a postdoc research fellow at the Weizmann Institute of Science. His research interest lies in theoretical computer science. He will start as an assistant professor at Pennsylvania State University, Fall 2024.

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相关标签

量子计算 概率分布 性质测试 相干性 多证明者模型
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