三种高效快速的量子态滤波和估计优化算法

doi:10.16451/j.cnki.issn1003-6059.201907005

摘要
图/表
参考文献(0)
相关文章 (2)

全文: PDF (765 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要

针对不同干扰和噪声情况下的量子状态估计和滤波问题,分别提出相应的高效量子状态密度矩阵重构凸优化算法.对于稀疏状态干扰和测量噪声同时存在的情况,提出量子状态滤波算法.对分别存在稀疏状态干扰和测量噪声的情况,提出相应两种不同的量子状态估计算法.在5量子位的状态密度矩阵估计仿真实验中分析不同采样率下的3种算法性能.实验表明,3种算法均具有较低的计算复杂度、较快的收敛速度和较低的估计误差.

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	丛爽
	丁娇
	张坤
	张娇娇

关键词 ：量子状态估计, 交替方向乘子法, 凸优化, 稀疏状态干扰, 测量噪声

Abstract：

The quantum state estimation and filtering problems under different interference and noise conditions are studied, and the corresponding efficient convex algorithms for quantum state density matrix reconstruction are proposed. A quantum state filtering algorithm is proposed for the simultaneous existence of sparse state interference and measurement noise. Two different quantum state estimation algorithms for the existence of sparse state interference and measurement noise are proposed, respectively. In the simulation experiments of the 5-qubit density matrix estimation, the performances of the three proposed algorithms are compared, and the performances of the three algorithms with different measurement rates are analyzed. Experimental results show that the three algorithms have high convergent speeds and low computational complexity, low estimation errors and measurement rates.

Key words： Quantum State Estimation Alternating Direction Multiplier Method Convex Optimization Sparse State Interference Measurement Noise

收稿日期: 2019-01-14

ZTFLH:

TP 301.6

基金资助:

国家自然科学基金项目(No.61573330)资助

作者简介: 丛爽(通讯作者),博士,教授,主要研究方向为量子系统状态调控与实现.E-mail:scong@ustc.edu.cn.ation of quantum states.)丁娇,硕士研究生,主要研究方向为量子状态估计.E-mail:dingjiao@mail.ustc.edu.cn.张坤,硕士研究生,主要研究方向为量子状态估计.E-mail:kzhang@mail.ustc.edu.cn.张娇娇,博士研究生,主要研究方向为量子状态估计.E-mail:jjzhang@mail.ustc.edu.cn.

引用本文:

丛爽,丁娇,张坤,张娇娇. 三种高效快速的量子态滤波和估计优化算法[J]. 模式识别与人工智能, 2019, 32(7): 615-623. CONG Shuang, DING Jiao, ZHANG Kun, ZHANG Jiaojiao. Three Types of Efficient and Fast Optimization Algorithms for Quantum State Filtering and Estimation. , 2019, 32(7): 615-623.

链接本文:

http://manu46.magtech.com.cn/Jweb_prai/CN/10.16451/j.cnki.issn1003-6059.201907005 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2019/V32/I7/615