Using machine learning for quantum annealing accuracy prediction

TL;DR

This paper uses machine learning to predict the accuracy of quantum annealers, specifically D-Wave's device, in solving the NP-hard Maximum Clique problem, by analyzing problem features and annealing parameters.

Contribution

It introduces machine learning models to predict quantum annealer performance and solution quality for specific problem instances, highlighting key factors affecting hardness.

Findings

Machine learning models can predict whether a problem is solvable to optimality.

Features like graph edges and annealing parameters influence solution hardness.

A simple decision tree effectively forecasts problem solvability.

Abstract

Quantum annealers, such as the device built by D-Wave Systems, Inc., offer a way to compute solutions of NP-hard problems that can be expressed in Ising or QUBO (quadratic unconstrained binary optimization) form. Although such solutions are typically of very high quality, problem instances are usually not solved to optimality due to imperfections of the current generations quantum annealers. In this contribution, we aim to understand some of the factors contributing to the hardness of a problem instance, and to use machine learning models to predict the accuracy of the D-Wave 2000Q annealer for solving specific problems. We focus on the Maximum Clique problem, a classic NP-hard problem with important applications in network analysis, bioinformatics, and computational chemistry. By training a machine learning classification model on basic problem characteristics such as the number of edges in the graph, or annealing parameters such as D-Wave's chain strength, we are able to rank certain features in the order of their contribution to the solution hardness, and present a simple decision tree which allows to predict whether a problem will be solvable to optimality with the D-Wave 2000Q. We extend these results by training a machine learning regression model that predicts the clique size found by D-Wave.