Hybrid convolutional neural network and PEPS wave functions for quantum many-particle states

• URL: http://arxiv.org/abs/2009.14370v2
• Date: Wed, 13 Jan 2021 14:55:34 GMT
• Title: Hybrid convolutional neural network and PEPS wave functions for quantum many-particle states
• Authors: Xiao Liang, Shao-Jun Dong and Lixin He
• Abstract summary: We propose a hybrid wave function combining the convolutional neural network (CNN) and projected entangled pair states (PEPS) We show that the achieved ground energies are competitive to state-of-the-art results.
• Score: 2.9449581560402747
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural networks have been used as variational wave functions for quantum many-particle problems. It has been shown that the correct sign structure is crucial to obtain the high accurate ground state energies. In this work, we propose a hybrid wave function combining the convolutional neural network (CNN) and projected entangled pair states (PEPS), in which the sign structures are determined by the PEPS, and the amplitudes of the wave functions are provided by CNN. We benchmark the ansatz on the highly frustrated spin-1/2 $J_1$-$J_2$ model. We show that the achieved ground energies are competitive to state-of-the-art results.

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