Method to Solve Quantum Few-Body Problems with Artificial Neural Networks
Method to Solve Quantum Few-Body Problems with Artificial Neural Networks
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DOI：
10.7566/jpsj.87.074002
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发表时间：
2018-04
影响因子：
1.7
通讯作者：
H. Saito
中科院分区：
文献类型：
--
作者：
H. Saito
发展了一种利用人工神经网络获得量子少体系统基态的机器学习方法。连续空间中的玻色子被认为是一个神经网络优化的方式，当粒子的位置输入到网络中，基态波函数是从网络输出。该方法适用于Calogero-Sutherland模型在一维空间和Efimov束缚态在三维空间。
A machine learning technique to obtain the ground states of quantum few-body systems using artificial neural networks is developed. Bosons in continuous space are considered and a neural network is optimized in such a way that when particle positions are input into the network, the ground-state wave function is output from the network. The method is applied to the Calogero-Sutherland model in one-dimensional space and Efimov bound states in three-dimensional space.