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量子信道的凸优化重建技术 认领 被引量:1

Reconstruction of quantum channel via convex optimization
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摘要 对于实际的量子信道,它可能包含一系列非幺正的操作,但是过去的研究基本都集中在幺正或近幺正的情况,对于非幺正过程的研究还是很少.本文借用经典人工智能技术中常用的凸优化方法,将量子过程层析构造成一个最小二乘的优化问题,并且能有效地缩小过程矩阵与实际实验结果之间的误差.作者首先选择了海水量子信道来测试该方法的可行性.对比经典的线性变换方法,该方法更准确地体现了海水量子信道的实际操作,并且能很好地保持量子过程的物理性.另外,作者在7个经典的量子门上进行了实验,分别对比了线性变换方法和范数优化的方法.对于线性变换,精度提高了1个量级;对于范数优化,减去了?参数的调优,精确度更高,鲁棒性更好.除此之外,作者还制备了非幺正的量子信道进行检验.对比以往常用的过程层析技术,在非幺正的情况,该方法依然能准确地还原量子过程,准确率达99.5%,进一步证明了其优越性. Quantum process tomography is often used to completely characterize an unknown quantum process.However, it may lead to an unphysical process matrix, which will cause the loss of information with respect to the tomography result. Convex optimization, widely used in machine learning, is able to generate a global optimum that best fits the raw data while keeping the process tomography in a legitimate region. Only by correctly revealing the original action of the process can we seek deeper into its properties like its phase transition and its Hamiltonian. Here, we reconstruct the seawater channel using convex optimization and further test it on the seven fundamental gates. We compare our method to the standard-inversion and norm-optimization approaches using the cost function value and our proposed state deviation. The advantages convince that our method enables a more precise and robust estimation of the elements of the process matrix with less demands on preliminary resources. In addition, we examine on a set of non-unitary channels and the reconstructions reach up to 99:5% accuracy. Our method offers a more universal tool for further analyses on the components of the quantum channels and we believe that the crossover between quantum process tomography and convex optimization may help us move forward to machine learning of quantum channels.
作者 黄选纶 高俊 焦志强 严增泉 张者勇 陈丹扬 张晰 嵇玲 金贤敏 Xuan-Lun Huang;Jun Gao;Zhi-Qiang Jiao;Zeng-Quan Yan;Zhe-Yong Zhang;Dan-Yang Chen;Xi Zhang;Ling Ji;Xian-Min Jin(不详)
出处 《科学通报:英文版》 SCIE EI CSCD 2020年第4期286-292,共7页 Science Bulletin
基金 supported by the National Key R&D Program of China(2019YFA0308700,2017YFA0303700) the National Natural Science Foundation of China(61734005,11761141014,and 11690033) the Science and Technology Commission of Shanghai Municipality(15QA1402200,16JC1400405,and 17JC1400403) the Shanghai Municipal Education Commission(16SG09 and 2017-01-07-00-02-E00049) additional support from a Shanghai Talent Program.
关键词 QUANTUM information QUANTUM process TOMOGRAPHY CONVEX optimization QUANTUM CHANNEL Quantum information Quantum process tomography Convex optimization Quantum channel
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