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arXiv 2409.08705quant-ph
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量子序列的最优区分

Optimal discrimination of quantum sequences

Tathagata Gupta, Shayeef Murshid, Vincent Russo, Somshubhro Bandyopadhyay

AI总结:

本文证明,当量子序列各成员从相同或不同系综中秘密且独立抽取时,最优区分成功概率可由固定局域测量实现,无需集体测量,且该结论适用于最小误差和无歧义区分两种范式。

摘要:

量子信息理论的一个关键概念是,获取编码在量子系统中的信息要求我们在系统可能处于的若干可能状态之间进行区分。这一问题的一个自然推广,即量子序列区分,出现在各种量子信息处理任务中,其目标是确定一个有限量子状态序列的状态。由于这样的序列是一个复合量子系统,根本问题是:最优测量是局域的,即由对各个成员进行的测量组成,还是集体的,即需要联合测量。在该问题的一些已知实例中,最优测量是局域的,而在另一些实例中则是集体的。但迄今为止,一直缺乏一种仅基于问题描述而给出的明确规则。在本文中,我们证明:如果给定序列的各个成员是从一个系综甚至是从不同系综中秘密且独立地抽取的,那么最优成功概率可以通过对序列各个成员进行固定的局域测量来实现,并且不需要任何集体测量。这一结论同时适用于最小误差和无歧义状态区分两种范式。

英文摘要:

A key concept of quantum information theory is that accessing information encoded in a quantum system requires us to discriminate between several possible states the system could be in. A natural generalization of this problem, namely, quantum sequence discrimination, appears in various quantum information processing tasks, the objective being to determine the state of a finite sequence of quantum states. Since such a sequence is a composite quantum system, the fundamental question is whether an optimal measurement is local, i.e., comprising measurements on the individual members, or collective, i.e. requiring joint measurement(s). In some known instances of this problem, the optimal measurement is local, whereas in others, it is collective. But, so far, a definite prescription based solely on the problem description has been lacking. In this paper, we prove that if the members of a given sequence are drawn secretly and independently from an ensemble or even from different ensembles, the optimum success probability is achievable by fixed local measurements on the individual members of the sequence, and no collective measurement is necessary. This holds for both minimum-error and unambiguous state discrimination paradigms.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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