期刊
PHYSICAL REVIEW E
卷 103, 期 1, 页码 -出版社
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevE.103.013309
关键词
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资金
- National Natural Science Foundation of China [11805279]
- Ministry of Education of Singapore AcRF MOE Tier-II [MOE2018-T2-2-142]
This work demonstrates how to extend automatic differentiation to complex loss functions, and applies it in solving quantum physics problems, providing practical value in efficiently computing gradients for neural networks.
The past few years have seen a significant transfer of tools from machine learning to solve quantum physics problems. Automatic differentiation is one standard algorithm used to efficiently compute gradients of loss functions for generic neural networks. In this work we show how to extend automatic differentiation to the case of complex loss function in a way that can be readily implemented in existing frameworks and which is compatible with the common case of real loss functions. We then combine this tool with matrix product states and apply it to compute the ground state and the steady state of a close and an open quantum system. Compared to the traditional density matrix renormalization group algorithm, complex automatic differentiation allows both straightforward GPU accelerations as well as generalizations to different types of tensor and neural networks.
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