Quantum restricted Boltzmann machine universal for quantum computation

• URL: http://arxiv.org/abs/2005.11970v3
• Date: Mon, 31 Aug 2020 02:48:15 GMT
• Title: Quantum restricted Boltzmann machine universal for quantum computation
• Authors: Yusen Wu, Chunyan Wei, Sujuan Qin, Qiaoyan Wen, and Fei Gao
• Abstract summary: Quantum neural network provides a powerful tool to represent the large-scale wave function. A significant open problem is what exactly the representational power boundary of the single-layer quantum neural network is.
• Score: 4.3411599646551196
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The challenge posed by the many-body problem in quantum physics originates from the difficulty of describing the nontrivial correlations encoded in the many-body wave functions with high complexity. Quantum neural network provides a powerful tool to represent the large-scale wave function, which has aroused widespread concern in the quantum superiority era. A significant open problem is what exactly the representational power boundary of the single-layer quantum neural network is. In this paper, we design a 2-local Hamiltonian and then give a kind of Quantum Restricted Boltzmann Machine (QRBM, i.e. single-layer quantum neural network) based on it. The proposed QRBM has the following two salient features. (1) It is proved universal for implementing quantum computation tasks. (2) It can be efficiently implemented on the Noisy Intermediate-Scale Quantum (NISQ) devices. We successfully utilize the proposed QRBM to compute the wave functions for the notable cases of physical interest including the ground state as well as the Gibbs state (thermal state) of molecules on the superconducting quantum chip. The experimental results illustrate the proposed QRBM can compute the above wave functions with an acceptable error.

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