Author Affiliations
Abstract
1 The Hong Kong University of Science and Technology, Department of Physics, Hong Kong, China
2 University of Guelph, Department of Mathematics and Statistics, Guelph, Ontario, Canada
3 University of Waterloo, Institute for Quantum Computing, Waterloo, Ontario, Canada
4 The University of Texas at Dallas, Department of Physics, Richardson, Texas, United States
Quantum state tomography (QST) is a crucial ingredient for almost all aspects of experimental quantum information processing. As an analog of the “imaging” technique in quantum settings, QST is born to be a data science problem, where machine learning techniques, noticeably neural networks, have been applied extensively. We build and demonstrate an optical neural network (ONN) for photonic polarization qubit QST. The ONN is equipped with built-in optical nonlinear activation functions based on electromagnetically induced transparency. The experimental results show that our ONN can determine the phase parameter of the qubit state accurately. As optics are highly desired for quantum interconnections, our ONN-QST may contribute to the realization of optical quantum networks and inspire the ideas combining artificial optical intelligence with quantum information studies.
optical neural network quantum state tomography quantum optics Advanced Photonics
2022, 4(2): 026004
山西大学 光电研究所,量子光学与光量子器件国家重点实验室,太原 030006
量子态层析和量子过程层析是刻画量子态和量子操作过程准确度的基本工具。本文主要对编码于二能级铯原子的单量子比特及其单量子操作进行了相关实验研究,对编码在铯原子钟态的量子态(-i|0〉+|1〉)2进行了量子态层析分析,得到其保真度为097±002。我们还对单量子比特的门操作Rx(π)、Rxπ2、Ry(π)、Ryπ2、Rzπ2进行了量子过程层析测量,得到量子门操作的平均保真度为096±003。我们对影响单比特态及其操作过程的保真度的因素进行了分析。
密度矩阵 量子态层析 量子过程层析 最大似然估计法 density matrix quantum state tomography quantum process tomography maximum-likelihood estimation