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arXiv 2011.06569quant-phcs.ITmath.IT

自适应策略在渐近量子信道区分中的有用性

Usefulness of adaptive strategies in asymptotic quantum channel discrimination

Farzin Salek, Masahito Hayashi, Andreas Winter

AI总结:

本研究证明在渐近量子信道区分中,自适应策略在多数情况下不优于非自适应策略,并首次给出两者对称假设检验指数的分离示例,表明经典反馈的自适应不增强信道区分能力。

摘要:

自适应是信息处理(包括统计学和机器学习)中的一个关键原则。我们研究了在渐近二元假设检验框架中自适应方法的有用性,其中每个假设代表一个量子信道的渐近多个独立实例,测试基于使用未知信道并观察输出。与以量子态作为假设的常见设置不同,在信道使用方面,自适应策略和非自适应策略之间存在根本区别,我们通过对测试策略施加不同限制,引入了区分任务的若干进一步变体。获得以下结果:(1)我们证明,对于经典-量子信道,自适应和非自适应策略在对称(Chernoff)和非对称(Hoeffding、Stein)设置中导致相同的误差指数。(2)量子信道自适应与非自适应对称假设检验指数之间的首次分离,我们从一个关于非自适应策略错误概率的一般下界推导得出;我们分析的具体示例是一对纠缠破坏信道。(3)我们在某种意义上推广前述陈述,证明对于一般信道,限制为经典前馈和乘积态信道输入的自适应策略在渐近极限中并不优于非自适应乘积态策略。(4)作为我们发现的一个应用,我们处理任意量子信道的区分能力,并表明具有经典反馈且输入处无量子内存的自适应策略不会将信道的区分能力提升到非自适应张量积输入策略之上。

英文摘要:

Adaptiveness is a key principle in information processing including statistics and machine learning. We investigate the usefulness of adaptive methods in the framework of asymptotic binary hypothesis testing, when each hypothesis represents asymptotically many independent instances of a quantum channel, and the tests are based on using the unknown channel and observing outputs. Unlike the familiar setting of quantum states as hypotheses, there is a fundamental distinction between adaptive and non-adaptive strategies with respect to the channel uses, and we introduce a number of further variants of the discrimination tasks by imposing different restrictions on the test strategies. The following results are obtained: (1) We prove that for classical-quantum channels, adaptive and non-adaptive strategies lead to the same error exponents both in the symmetric (Chernoff) and asymmetric (Hoeffding, Stein) settings. (2) The first separation between adaptive and non-adaptive symmetric hypothesis testing exponents for quantum channels, which we derive from a general lower bound on the error probability for non-adaptive strategies; the concrete example we analyze is a pair of entanglement-breaking channels. (3)We prove, in some sense generalizing the previous statement, that for general channels adaptive strategies restricted to classical feed-forward and product state channel inputs are not superior in the asymptotic limit to non-adaptive product state strategies. (4) As an application of our findings, we address the discrimination power of an arbitrary quantum channel and show that adaptive strategies with classical feedback and no quantum memory at the input do not increase the discrimination power of the channel beyond non-adaptive tensor product input strategies.

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