Statistical approach to quantum phase estimation
Statistical approach to quantum phase estimation
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DOI：
10.1088/1367-2630/ac320d
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发表时间：
2021-04
影响因子：
3.3
通讯作者：
Alexandria J Moore;Yuchen Wang;Zixuan Hu;S. Kais;A. Weiner
中科院分区：
文献类型：
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作者：
Alexandria J Moore;Yuchen Wang;Zixuan Hu;S. Kais;A. Weiner
我们介绍了一种新的统计和变分方法的相位估计算法（PEA）。与传统的和迭代的PEA只返回一个特征相位估计，所提出的方法可以确定任何未知的本征态-本征相位对从一个给定的酉矩阵，利用一个简化版本的硬件用于迭代PEA（IPEA）。这是通过将IPEA类电路的概率输出视为本征态-本征相接近度量来实现的，使用该度量来估计输入状态和输入相位与最近的本征态-本征相对的接近度，并通过对输入状态和相位的变分过程来接近该对。这种方法可以在整个计算空间中进行搜索，或者可以有效地搜索某些指定范围（方向）内的本征相（本征态），从而允许那些对其系统有一些先验知识的人搜索特定的解。我们展示了该方法的仿真结果与Qiskit包在IBM Q平台和本地计算机上。
We introduce a new statistical and variational approach to the phase estimation algorithm (PEA). Unlike the traditional and iterative PEAs which return only an eigenphase estimate, the proposed method can determine any unknown eigenstate–eigenphase pair from a given unitary matrix utilizing a simplified version of the hardware intended for the iterative PEA (IPEA). This is achieved by treating the probabilistic output of an IPEA-like circuit as an eigenstate–eigenphase proximity metric, using this metric to estimate the proximity of the input state and input phase to the nearest eigenstate–eigenphase pair and approaching this pair via a variational process on the input state and phase. This method may search over the entire computational space, or can efficiently search for eigenphases (eigenstates) within some specified range (directions), allowing those with some prior knowledge of their system to search for particular solutions. We show the simulation results of the method with the Qiskit package on the IBM Q platform and on a local computer.