Quantum Restricted Boltzmann Machine is Universal for Quantum Computation

By Y. Wu et al.

Published on Aug. 31, 2020

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Table of Contents

1. Introduction
2. Quantum Neural Network
3. Proposed Quantum Restricted Boltzmann Machine (QRBM)
4. Experimental Results
5. Conclusion

Summary

The document discusses the challenges posed by the many-body problem in quantum physics and the role of Quantum Neural Network in representing large-scale wave functions. It introduces a Quantum Restricted Boltzmann Machine (QRBM) as a single-layer quantum neural network that is universal for implementing quantum computation tasks. The proposed QRBM is shown to efficiently compute wave functions for physical systems and is compared to classical RBM and other quantum algorithms. The paper also explores the quantum advantages of 2L-QRBM in simulating complex quantum states and demonstrates its universality for quantum computation tasks. Experimental results include the computation of ground state energies for the Hydrogen and Water molecules using the Quantum Imaginary Time Evolution (QITE) algorithm. The document highlights the potential of 2L-QRBM in quantum computation and its advantages over classical methods.