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题名

基于量子哈密顿量层析的量子表征及其实验研究

其他题名
QUANTUM CHARACTERIZATION BASED ON HAMILTONIAN TOMOGRAPHY AND ITS EXPERIMENTAL RESEARCH
姓名
姓名拼音
WEI Chao
学号
11930019
学位类型
硕士
学位专业
070203 原子与分子物理
学科门类/专业学位类别
07 理学
导师
辛涛
导师单位
量子科学与工程研究院
论文答辩日期
2022-05-11
论文提交日期
2022-07-01
学位授予单位
南方科技大学
学位授予地点
深圳
摘要
量子表征是量子计算任务中的重要一环,是实现量子计算实用化不可或缺的关键技术。在早期的量子计算发展中,学界更关注的是量子比特的物理实现以及量子门电路的搭建,但随着量子计算体系中的比特数目逐渐增多,实现算法所需的量子门电路层数越来越多,计算过程中的环境噪声以及量子操控中的累积误差变得越来越不可忽视。要实现对量子设备工作精度的改进,首先我们需要将量子设备的性能进行表征。相较于量子比特的制备以及通过量子控制实现量子门操作等任务,量子表征还处于一个发展较为滞后的状态,但经过多年发展人们同样提出了多种技术方案,量子哈密顿量层析就是其中之一。
本论文将介绍两个基于量子哈密顿量层析的量子表征工作。其中,在核磁共振量子计算平台上我们进行了基于量子淬火的量子哈密顿量层析工作,对选定的两比特 Ising 模型的哈密顿量参数进行表征,得出了相当精确的实验结果。同时我们还进行了其他的传统表征实验并对比了实验结果,对比结果显示,淬火方案的量子哈密顿量层析方案得出的的实验应用有着不输于传统表征手段的表征能力。之后,为了进一步探索并优化哈密顿量层析的实现方案,我们设计了一个利用机器学习技术通过对单比特测量数据的学习估测哈密顿量参数的哈密顿量表征方案。我们选取了几个特殊的哈密顿量模型生成单比特测量数据并输入神经网络模型进行了训练,并测试了该方案面对含噪声数据时表征精度的鲁棒性。训练和测试的结果验证了我们的机器学习方案对多种模型都可以有效学习到其哈密顿量特征,并且具有极高的表征准确度和鲁棒性。
关键词
语种
中文
培养类别
独立培养
入学年份
2019
学位授予年份
2022-07
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魏超. 基于量子哈密顿量层析的量子表征及其实验研究[D]. 深圳. 南方科技大学,2022.
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