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arXiv quant-ph/0206093quant-ph
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量子态最佳非对称鉴别的半定规划方法

A Semidefinite Programming Approach to Optimal Unambiguous Discrimination of Quantum States

Yonina C. Eldar

摘要:

本文研究线性无关纯量子态的无模糊判别问题。我们表明,设计的最佳测量,最大限度地减少了不确定的结果的概率,可以制定为一个半定规划问题。基于这个公式,我们发展了一组最佳量子测量的充分必要条件。我们表明,最佳的测量可以非常有效地计算在多项式时间内利用许多著名的算法来解决半定方案,这是保证收敛到全局最优。使用最优性的一般条件,我们得到必要和充分条件,使测量结果在一个不确定的结果为每一个量子态的概率相等是最佳的。我们把这种测量称为等概率测量(Equal Probability Measurement,简称EMT)。然后,我们表明,对于任何状态集,状态的先验概率可以选择,使得最优的。最后,我们考虑具有强对称性和等先验概率的状态集,对于这些状态集,最优解是最优的。我们首先考虑定义在一组酉矩阵上的几何一致状态集,它们由单个生成向量生成。然后,我们考虑复合几何均匀状态集所产生的一组酉矩阵使用多个生成向量,其中的生成向量满足一定的(加权)范数约束。

英文摘要:

In this paper we consider the problem of unambiguous discrimination between a set of linearly independent pure quantum states. We show that the design of the optimal measurement that minimizes the probability of an inconclusive result can be formulated as a semidefinite programming problem. Based on this formulation, we develop a set of necessary and sufficient conditions for an optimal quantum measurement. We show that the optimal measurement can be computed very efficiently in polynomial time by exploiting the many well-known algorithms for solving semidefinite programs, which are guaranteed to converge to the global optimum. Using the general conditions for optimality, we derive necessary and sufficient conditions so that the measurement that results in an equal probability of an inconclusive result for each one of the quantum states is optimal. We refer to this measurement as the equal-probability measurement (EPM). We then show that for any state set, the prior probabilities of the states can be chosen such that the EPM is optimal. Finally, we consider state sets with strong symmetry properties and equal prior probabilities for which the EPM is optimal. We first consider geometrically uniform state sets that are defined over a group of unitary matrices and are generated by a single generating vector. We then consider compound geometrically uniform state sets which are generated by a group of unitary matrices using multiple generating vectors, where the generating vectors satisfy a certain (weighted) norm constraint.

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