量子
统计物理学
赫巴德模型
水准点(测量)
量子计算机
量子算法
而量子蒙特卡罗
计算机科学
特征向量
玻尔兹曼常数
物理
量子力学
数学
蒙特卡罗方法
超导电性
统计
大地测量学
地理
作者
Shu Kanno,Tomofumi Tada
标识
DOI:10.1088/2058-9565/abe139
摘要
A state-of-the-art method that combines a quantum computational algorithm and machine learning, so-called quantum machine learning, can be a powerful approach for solving quantum many-body problems. However, the research scope in the field was mainly limited to organic molecules and simple lattice models. Here, we propose a workflow of quantum machine learning applications for periodic systems on the basis of an effective model construction from first principles. The band structures of the Hubbard model of graphene with the mean-field approximation are calculated as a benchmark, and the calculated eigenvalues show good agreement with the exact diagonalization results within a few meV by employing the transfer learning technique in quantum machine learning. The results show that the present computational scheme has the potential to solve many-body problems quickly and correctly for periodic systems using a quantum computer.
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