Boosting the Performance of Quantum Annealers using Machine Learning

TL;DR

This paper introduces a machine learning-based error correction method for quantum annealers, significantly enhancing their problem-solving capabilities by mitigating intrinsic imperfections and environmental noise.

Contribution

It presents a novel machine learning approach to optimize the input Hamiltonian, improving quantum annealer performance and enabling solutions to previously intractable problems.

Findings

Performance improved by up to three orders of magnitude

Enabled solving of maximally complex problems

Demonstrated effectiveness on real quantum annealers

Abstract

Noisy intermediate-scale quantum (NISQ) devices are spearheading the second quantum revolution. Of these, quantum annealers are the only ones currently offering real world, commercial applications on as many as 5000 qubits. The size of problems that can be solved by quantum annealers is limited mainly by errors caused by environmental noise and intrinsic imperfections of the processor. We address the issue of intrinsic imperfections with a novel error correction approach, based on machine learning methods. Our approach adjusts the input Hamiltonian to maximize the probability of finding the solution. In our experiments, the proposed error correction method improved the performance of annealing by up to three orders of magnitude and enabled the solving of a previously intractable, maximally complex problem.