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Multiple-copy state discrimination: Thinking globally, acting locally

机译:多副本状态歧视:全局思考,本地行动

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摘要

We theoretically investigate schemes to discriminate between two nonorthogonal quantum states given multiple copies. We consider a number of state discrimination schemes as applied to nonorthogonal, mixed states of a qubit. In particular, we examine the difference that local and global optimization of local measurements makes to the probability of obtaining an erroneous result, in the regime of finite numbers of copies N, and in the asymptotic limit as N?8. Five schemes are considered: optimal collective measurements over all copies, locally optimal local measurements in a fixed single-qubit measurement basis, globally optimal fixed local measurements, locally optimal adaptive local measurements, and globally optimal adaptive local measurements. Here an adaptive measurement is one in which the measurement basis can depend on prior measurement results. For each of these measurement schemes we determine the probability of error (for finite N) and the scaling of this error in the asymptotic limit. In the asymptotic limit, it is known analytically (and we verify numerically) that adaptive schemes have no advantage over the optimal fixed local scheme. Here we show moreover that, in this limit, the most naive scheme (locally optimal fixed local measurements) is as good as any noncollective scheme except for states with less than 2% mixture. For finite N, however, the most sophisticated local scheme (globally optimal adaptive local measurements) is better than any other noncollective scheme for any degree of mixture.
机译:我们从理论上研究了在给定多个副本的情况下区分两个非正交量子态的方案。我们考虑了许多状态区分方案,这些方案适用于qubit的非正交混合状态。特别是,我们在有限数量的副本N和N = 8的渐近极限中研究了局部测量的局部和全局优化对获得错误结果的概率的不同。考虑了五种方案:所有副本上的最佳集体测量,以固定单量子位测量为基础的局部最优局部测量,全局最优固定局部测量,局部最优自适应局部测量和全局最优自适应局部测量。在此,自适应测量是其中测量基础可以取决于先前的测量结果的一种。对于这些测量方案中的每一个,我们确定误差的概率(对于有限的N)以及该误差在渐近极限中的缩放比例。在渐近极限中,从分析上(并且我们通过数字验证)得知,自适应方案比最优固定局部方案没有优势。此外,我们在这里表明,在此限制下,除了混合状态少于2%的状态以外,最幼稚的方案(局部最优的固定局部测量)与任何非集体方案一样好。但是,对于有限的N,对于任何程度的混合,最复杂的局部方案(全局最佳自适应局部测量)要好于任何其他非集体方案。

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