首页|量子多体计算:多领域交叉融合的强关联物理研究前沿

量子多体计算:多领域交叉融合的强关联物理研究前沿

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量子多体计算方法,包括精确对角化、量子蒙特卡罗、密度矩阵与张量重正化、动力学平均场等传统方法,以及结合人工智能、量子计算等新兴计算方法,被发展用于精确高效地计算关联多体系统的物理性质。在非微扰的相互作用多体系统中,粒子之间通常存在强的关联与量子纠缠,基于平均场理论的计算方法往往不够精准、甚至缺乏可靠性,需要不断发展新的多体计算方法,以便准确地研究如关联费米子系统、阻挫自旋系统中的新奇物态与演生规律。近年来,多体计算与多体理论、机器学习、材料科学、量子模拟与量子计算等领域不断互动与融合,呈现出越来越丰富的学科交叉特点。本文对量子多体计算的历史脉络、主要方法及现状、未来发展的挑战进行概述。
Quantum many-body computation:A frontier in interdisci-plinary research on strongly correlated systems
Quantum many-body calculation methods,including exact diagonalization,quantum Monte Carlo,density matrix and tensor renormalization,and dynamic mean field theory,as well as emerging methods harnessing the power of artificial intelligence and quantum computing,can be used to accurately and efficiently calculate the physical properties of correlated quantum many-body systems.The quantum particles in the interacting,non-perturbative many-body system are highly entangled,and the mean field theory often lacks sufficient accuracy or may even break down for certain problems.It is thus very necessary to develop new methods for studying the exotic states and emergent phenomena in correlated systems,such as high-temperature superconductivity and frustrated quantum magnetism.Recently,the rapid progress in the field of many-body calculation has shown the characteristics of interdisciplinary cross,and there is increasingly significant interplay and integration with machine learning,materials science,quantum simulation and computation,among other fields.This paper outlines the brief history of many-body calculations,major approaches and their current status,as well as main challenges in this field.

many-body calculationexact diagonalizationMonte Carlo methodtensor networkdynamic mean field theorymachine learningquantum computation and simulation

李伟、王磊、蔡子、王孝群、苏刚

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中国科学院理论物理研究所,北京 100190

中国科学院物理研究所,北京 100190

上海交通大学物理与天文学院,上海 200240

浙江大学物理学院,杭州 310027

中国科学院大学卡弗里理论科学研究所,北京 100049

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多体计算 精确对角化 蒙特卡罗方法 张量网络 动力学平均场 机器学习 量子计算与量子模拟

2024

中国科学(物理学 力学 天文学)
中国科学院

中国科学(物理学 力学 天文学)

CSTPCD北大核心
影响因子:0.644
ISSN:1674-7275
年,卷(期):2024.54(4)
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