Quantum Machine Learning

TL;DR

Quantum machine learning combines quantum computing with ML to potentially outperform classical methods, with promising applications in cybersecurity and ongoing research into its fundamentals, progress, and challenges.

Contribution

This paper introduces the fundamentals of QML, reviews recent progress, discusses future trends, and explores its potential in cybersecurity applications.

Findings

QML offers opportunities for quantum advantage in ML tasks.

Recent progress indicates growing momentum in QML research.

Open challenges include achieving practical quantum advantage and integration in security applications.

Abstract

The meteoric rise of artificial intelligence in recent years has seen machine learning methods become ubiquitous in modern science, technology, and industry. Concurrently, the emergence of programmable quantum computers, coupled with the expectation that large-scale fault-tolerant machines will follow in the near to medium-term future, has led to much speculation about the prospect of quantum machine learning (QML), namely machine learning (ML) solutions which take advantage of quantum properties to outperform their classical counterparts. Indeed, QML is widely considered as one of the front-running use cases for quantum computing. In recent years, research in QML has gained significant global momentum. In this chapter, we introduce the fundamentals of QML and provide a brief overview of the recent progress and future trends in the field of QML. We highlight key opportunities for achieving quantum advantage in ML tasks, as well as describe some open challenges currently facing the field of QML. Specifically in the context of cybersecurity, we introduce the potential for QML in defence and security-sensitive applications, where it has been predicted that the seamless integration of quantum computing into ML will herald the development of robust and reliable QML systems, resilient against sophisticated threats arising from data manipulation and poisoning.