A Review of Machine Learning Classification Using Quantum Annealing for Real-world Applications

TL;DR

This paper reviews how quantum annealing, particularly using D-Wave systems, can optimize machine learning classification tasks in real-world applications, highlighting experimental results and potential advantages over classical methods.

Contribution

It provides a comprehensive review of applying D-Wave's quantum annealer to optimize machine learning pipelines across various real-world classification domains.

Findings

Quantum annealing shows promise in handling high-dimensional data.

Experimental results demonstrate potential advantages over classical methods.

Applications include image recognition, biology, and physics.

Abstract

Optimizing the training of a machine learning pipeline helps in reducing training costs and improving model performance. One such optimizing strategy is quantum annealing, which is an emerging computing paradigm that has shown potential in optimizing the training of a machine learning model. The implementation of a physical quantum annealer has been realized by D-Wave systems and is available to the research community for experiments. Recent experimental results on a variety of machine learning applications using quantum annealing have shown interesting results where the performance of classical machine learning techniques is limited by limited training data and high dimensional features. This article explores the application of D-Wave's quantum annealer for optimizing machine learning pipelines for real-world classification problems. We review the application domains on which a physical quantum annealer has been used to train machine learning classifiers. We discuss and analyze the experiments performed on the D-Wave quantum annealer for applications such as image recognition, remote sensing imagery, computational biology, and particle physics. We discuss the possible advantages and the problems for which quantum annealing is likely to be advantageous over classical computation.