Quantum Machine Learning for Cybersecurity: A Taxonomy and Future Directions

2025-12-17

AI Summary

Traditional machine learning and rule-/signature-based detection methods suffer from poor generalization and delayed response in high-dimensional, dynamic, and large-scale cyber threat scenarios. Method: This paper systematically investigates the adaptation mechanisms of quantum machine learning (QML) to cybersecurity, proposing a comprehensive QML classification framework covering intrusion detection, malware identification, and encrypted traffic analysis. It establishes a bidirectional mapping between supervised, unsupervised, and generative learning paradigms and security tasks, and designs deployment pathways for four QML models—Quantum Neural Networks (QNNs), Quantum Support Vector Machines (QSVMs), Variational Quantum Circuits (VQCs), and Quantum Generative Adversarial Networks (QGANs)—in cloud security contexts. Contribution/Results: We present the first holistic QML landscape for cybersecurity, identifying six critical application bottlenecks and distilling four engineering-feasible evolutionary pathways tailored for Noisy Intermediate-Scale Quantum (NISQ) devices—thereby establishing both a theoretical benchmark and a practical roadmap for secure QML adoption.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Adversarial Attacks & Robustness
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Security and Privacy: Security and privacy of machine learning and AI applications
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• Surveying Quantum Machine Learning techniques for cybersecurity applications
• Mapping QML methods to cybersecurity tasks like intrusion detection
• Addressing QML limitations and future directions in cybersecurity

Innovation

• Quantum machine learning enhances cybersecurity threat detection
• Quantum neural networks and support vector machines improve data processing
• Variational quantum circuits enable scalable cloud security operations