The increasing number of cyber threats and rapidly evolving tactics, as well as the high volume of data in recent years, have caused classical machine learning, rules, and signature-based defence strategies to fail, rendering them unable to keep up. An alternative, Quantum Machine Learning (QML), has recently emerged, making use of computations based on quantum mechanics. It offers better encoding and processing of high-dimensional structures for certain problems. This survey provides a comprehensive overview of QML techniques relevant to the domain of security, such as Quantum Neural Networks (QNNs), Quantum Support Vector Machines (QSVMs), Variational Quantum Circuits (VQCs), and Quantum Generative Adversarial Networks (QGANs), and discusses the contributions of this paper in relation to existing research in the field and how it improves over them. It also maps these methods across supervised, unsupervised, and generative learning paradigms, and to core cybersecurity tasks, including intrusion and anomaly detection, malware and botnet classification, and encrypted-traffic analytics. It also discusses their application in the domain of cloud computing security, where QML can enhance secure and scalable operations. Many limitations of QML in the domain of cybersecurity have also been discussed, along with the directions for addressing them.

翻译：近年来，网络威胁数量激增、攻击手段快速演变以及数据规模庞大，导致基于经典机器学习、规则和特征签名的防御策略难以应对，已无法跟上安全需求的发展。作为一种替代方案，量子机器学习（QML）应运而生，其利用基于量子力学的计算原理，在特定问题上能够实现对高维结构更优的编码与处理。本综述系统性地梳理了与安全领域相关的QML技术，包括量子神经网络（QNNs）、量子支持向量机（QSVMs）、变分量子电路（VQCs）以及量子生成对抗网络（QGANs），并阐述了本文相对于该领域现有研究的贡献及其改进之处。同时，本文将上述方法映射至监督学习、无监督学习与生成式学习范式，并关联到核心网络安全任务，涵盖入侵与异常检测、恶意软件与僵尸网络分类以及加密流量分析。此外，本文还探讨了QML在云计算安全领域的应用，其可助力实现更安全、可扩展的云运营。文中亦详细讨论了QML在网络安全应用中存在的诸多局限性，并指出了相应的解决方向。