Solving quantum statistical mechanics with variational autoregressive networks and quantum circuits
Liu, Jin-Guo; Mao, Liang2; Zhang, Pan3; Wang, Lei1
刊名MACHINE LEARNING-SCIENCE AND TECHNOLOGY
2021
卷号2期号:2页码:25011
关键词LATTICE
DOI10.1088/2632-2153/aba19d
英文摘要We extend the ability of an unitary quantum circuit by interfacing it with a classical autoregressive neural network. The combined model parametrizes a variational density matrix as a classical mixture of quantum pure states, where the autoregressive network generates bitstring samples as input states to the quantum circuit. We devise an efficient variational algorithm to jointly optimize the classical neural network and the quantum circuit to solve quantum statistical mechanics problems. One can obtain thermal observables such as the variational free energy, entropy, and specific heat. As a byproduct, the algorithm also gives access to low energy excitation states. We demonstrate applications of the approach to thermal properties and excitation spectra of the quantum Ising model with resources that are feasible on near-term quantum computers.
学科主题Computer Science ; Science & Technology - Other Topics
语种英语
内容类型期刊论文
源URL[http://ir.itp.ac.cn/handle/311006/27660]  
专题理论物理研究所_理论物理所1978-2010年知识产出
作者单位1.Chinese Acad Sci, Inst Theoret Phys, CAS Key Lab Theoret Phys, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Phys, Beijing 100190, Peoples R China
3.Tsinghua Univ, Dept Phys, Beijing 100084, Peoples R China
4.Songshan Lake Mat Lab, Dongguan 523808, Guangdong, Peoples R China
推荐引用方式
GB/T 7714
Liu, Jin-Guo,Mao, Liang,Zhang, Pan,et al. Solving quantum statistical mechanics with variational autoregressive networks and quantum circuits[J]. MACHINE LEARNING-SCIENCE AND TECHNOLOGY,2021,2(2):25011.
APA Liu, Jin-Guo,Mao, Liang,Zhang, Pan,&Wang, Lei.(2021).Solving quantum statistical mechanics with variational autoregressive networks and quantum circuits.MACHINE LEARNING-SCIENCE AND TECHNOLOGY,2(2),25011.
MLA Liu, Jin-Guo,et al."Solving quantum statistical mechanics with variational autoregressive networks and quantum circuits".MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2.2(2021):25011.
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