Classical Quantum Optimization with Neural Network Quantum States
Abstract
The classical simulation of quantum systems typically requires exponential resources. Recently, the introduction of a machine learning-based wavefunction ansatz has led to the ability to solve the quantum many-body problem in regimes that had previously been intractable for existing exact numerical methods. Here, we demonstrate the utility of the variational representation of quantum states based on artificial neural networks for performing quantum optimization. We show empirically that this methodology achieves high approximation ratio solutions with polynomial classical computing resources for a range of instances of the Maximum Cut (MaxCut) problem whose solutions have been encoded into the ground state of quantum many-body systems up to and including 256 qubits.
Cite
@article{arxiv.1910.10675,
title = {Classical Quantum Optimization with Neural Network Quantum States},
author = {Joseph Gomes and Keri A. McKiernan and Peter Eastman and Vijay S. Pande},
journal= {arXiv preprint arXiv:1910.10675},
year = {2019}
}
Comments
Second Workshop on Machine Learning and the Physical Sciences (NeurIPS 2019), Vancouver, Canada