Application of large language models to quantum state simulation  

作　　者：Shuangxiang Zhou Ronghang Chen Zheng An Chao Zhang Shi-Yao Hou 

机构地区：[1]College of Physics and Electronic Engineering,Center for Computational Sciences,Sichuan Normal University,Chengdu 610068,China [2]Department of Physics,The Hong Kong University of Science and Technology,Hong Kong 999077,China

出　　处：《Science China(Physics,Mechanics & Astronomy)》2025年第4期45-61,共17页中国科学:物理学、力学、天文学(英文版)

基　　金：supported by the National Natural Science Foundation of China(Grant No.12105195)。

摘　　要：Quantum computers leverage the unique advantages of quantum mechanics to achieve acceleration over classical computers for certain problems.Currently,various quantum simulators provide powerful tools for researchers,but simulating quantum evolution with these simulators often incurs high time costs.Additionally,resource consumption grows exponentially as the number of quantum bits increases.To address this issue,our research aims to utilize Large Language Models(LLMs)to simulate quantum circuits.This paper details the process of constructing 1-qubit and 2-qubit quantum simulator models,extending to multiple qubits,and ultimately implementing a 3-qubit example.Our study demonstrates that LLMs can effectively learn and predict the evolution patterns among quantum bits,with minimal error compared to the theoretical output states.Even when dealing with quantum circuits comprising an exponential number of quantum gates,LLMs remain computationally efficient.Overall,our results highlight the potential of LLMs to predict the outputs of complex quantum dynamics,achieving speeds far surpassing those required to run the same process on a quantum computer.This finding provides new insights and tools for applying machine learning methods in the field of quantum computing.

关 键 词：large language models quantum simulation quantum computing 

分 类 号：O413[理学—理论物理] TP38[理学—物理]