Classical Quantum Optimization with Neural Network Quantum States

TL;DR

This paper demonstrates that neural network quantum states can be used to efficiently approximate solutions to large quantum optimization problems, specifically MaxCut, with high accuracy and polynomial resources.

Contribution

It introduces a neural network-based variational approach for quantum optimization, enabling solutions for large quantum systems previously intractable by classical methods.

Findings

Achieves high approximation ratios for MaxCut problems up to 256 qubits.

Uses polynomial classical resources for quantum state approximation.

Demonstrates the effectiveness of neural network quantum states in quantum optimization.

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.