Neural network representations of quantum many-body states

• 来源期刊: 中国科学:物理学 力学 天文学（英文版）
• 年，卷（期）: 2020,（1）
• 页码范围: 55-69
• 页数: 15页
• 语种: 英文

摘要

Machine learning is currently the most active interdisciplinary field having numerous applications;additionally,machine-learning techniques are used to research quantum many-body problems.In this study,we first propose neural network quantum states (NNQSs) with general input observables and explore a few related properties,such as the tensor product and local unitary operation.Second,we determine the necessary and sufficient conditions for the representability of a general graph state using normalized NNQS.Finally,to quantify the approximation degree of a given pure state,we define the best approximation degree using normalized NNQSs.Furthermore,we observe that some N-qubit states can be represented by a normalized NNQS,such as separable pure states,Bell states and GHZ states.