In the last few years, quantum computing has experienced a growth spurt. One exciting avenue of quantum computing is quantum machine learning (QML) which can exploit the high dimensional Hilbert space to learn richer representations from limited data and thus can efficiently solve complex learning tasks. Despite the increased interest in QML, there have not been many studies that discuss the security aspects of QML. In this work, we explored the possible future applications of QML in the hardware security domain. We also expose the security vulnerabilities of QML and emerging attack models, and corresponding countermeasures.
In the last few years, quantum computing has experienced a growth spurt. One exciting avenue of quantum computing is quantum machine learning (QML) which can exploit the high dimensional Hilbert space to learn richer representations from limited data and thus can efficiently solve complex learning tasks. Despite the increased interest in QML, there have not been many studies that discuss the security aspects of QML. In this work, we explored the possible future applications of QML in the hardware security domain. We also expose the security vulnerabilities of QML and emerging attack models, and corresponding countermeasures.
Abstract The rapid advancement of artificial intelligence has made machine learning (ML) methods central to modern science, technology, and industry. At the same time, the emergence of programmable quantum computers, along with the anticipation of large-scale fault-tolerant machines in the future, has generated interest in the potential of quantum machine learning (QML)—a field that explores quantum properties to enhance ML tasks. While QML is often considered a promising application for quantum computing, its practical advantages over classical methods remain an area of active research and exploration. In recent years, QML has garnered significant global attention, with researchers investigating its potential and limitations. In this chapter, we introduce the fundamentals of QML and provide an overview of recent developments and future trends in the field. We outline potential opportunities where quantum properties might offer advantages in ML tasks while also addressing the open challenges that QML faces in terms of scalability, implementation, and practical utility. We also explore the potential applications of QML in defense and security-sensitive domains. Here, researchers speculate that integrating quantum computing into ML could lead to the development of systems capable of resisting sophisticated threats, such as data manipulation and poisoning. However, realizing these possibilities requires further advancements in both quantum hardware and algorithmic design, making QML a promising yet still evolving field.
ABSTRACT Context The rapid increase in cyber threats, coupled with increasingly sophisticated attack strategies and the exponential growth of data in recent years, has exposed significant limitations in classical machine learning, rule‐based, and signature‐based defense mechanisms. These approaches are often unable to scale or adapt effectively to evolving threat landscapes. Objective As a result, Quantum Machine Learning (QML) has emerged as a promising alternative, leveraging computational principles from quantum mechanics. QML enables more expressive encoding and efficient processing of high‐dimensional data, offering potential advantages for complex cybersecurity tasks. Method 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. Results It also maps these methods across supervised, unsupervised, and generative learning paradigms, as well as to core cybersecurity tasks, including intrusion and anomaly detection, malware and botnet classification, and encrypted‐traffic analytics. It also discusses their application in cloud computing security, where QML can enhance secure, scalable operations. Conclusion Many limitations of QML in the domain of cybersecurity have also been discussed, along with the directions for addressing them.
Quantum Machine Learning (QML) has advanced significantly thanks to the combination of Quantum Computing (QC) with Artificial Intelligence (AI), hence releasing computational benefits over conventional methods. This synergy does, however, also bring fresh security flaws like adversarial attacks, quantum noise manipulation, and cryptographic weaknesses. This work offers a thorough investigation of QML security along with an examination of its special vulnerabilities resulting from hardware-induced faults, quantum variational circuits, and quantum data encoding. We methodically investigate adversarial attack techniques using the probabilistic character of quantum states including side-channel assaults, quantum noise injection, and algorithmic perturbations. We also assess innovative defensive strategies such differential privacy, quantum adversarial training, quantum error correction (QEC), cryptographic techniques include Quantum Homomorphic Encryption (QHE), We offer a hybrid AI-driven method for protecting QML models against developing threats by linking artificial intelligence and quantum security frameworks. This work emphasizes the importance of developing quantum-safe AI systems and consistent adversarial robustness standards. The results help to advance AI-enhanced quantum security, thereby guaranteeing the future of QML applications is efficient, strong, and resistant to adversarial attack.
Quantum Machine Learning (QML) has emerged as a promising intersection of quantum computing and classical machine learning, anticipated to drive breakthroughs in computational tasks. This paper discusses the question which security concerns and strengths are connected to QML by means of a systematic literature review. We categorize and review the security of QML models, their vulnerabilities inherent to quantum architectures, and the mitigation strategies proposed. The survey reveals that while QML possesses unique strengths, it also introduces novel attack vectors not seen in classical systems. We point out specific risks, such as cross-talk in superconducting systems and forced repeated shuttle operations in ion-trap systems, which threaten QML's reliability. However, approaches like adversarial training, quantum noise exploitation, and quantum differential privacy have shown potential in enhancing QML robustness. Our review discuss the need for continued and rigorous research to ensure the secure deployment of QML in real-world applications. This work serves as a foundational reference for researchers and practitioners aiming to navigate the security aspects of QML.
Machine Learning (ML) has been extensively utilized in various scientific and engineering domains. But the inherent constraints and computational complexity that arise in classical machine learning are particularly evident when dealing with large-scale, high-dimensional datasets or when attempting to solve intricate optimisation problems. Quantum systems exhibit unconventional patterns that are typically not effectively generated by traditional systems and can also tackle high dimensional and large size datasets efficiently. Therefore, it is believed that quantum computing systems may outperform traditional computing devices in machine learning tasks. Quantum Machine Learning (QML) has emerged as a rapidly developing field that combines the concepts of quantum mechanics and machine learning to solve complex problems across various disciplines. The domain of quantum machine learning investigates the development and execution of quantum software with the potential to facilitate machine learning at a much superior pace compared to traditional computers. However, the major challenges encountered are hardware limitations, noise, and development of quantum algorithms. The objective of this paper is to provide an insight on the foundational principles, essential algorithms, various associated platforms, potential applications and challenges being faced by QML. As data continues to grow in size and complexity, together with the emergence of nonlinear and high-dimensional real-world situations, the computing benefits offered by QML have the potential to facilitate significant advancements in the fields of healthcare, finance, robotics, logistics, communication, and cyber security. This paper presents the basis for future research work related to development of quantum algorithms in the domain of machine learning.
With the rapid advancement of artificial intelligence (AI) and quantum computing, cybersecurity threats have evolved, giving rise to adversarial quantum attacks. These attacks exploit the vulnerabilities of AI models using quantum algorithms, posing a significant risk to data security, model robustness, and decision-making systems. This paper presents a comprehensive review of adversarial quantum attacks on AI, analyzing their mechanisms, potential impacts, and countermeasures. It explores how quantum computing can enhance adversarial attacks by accelerating the generation of adversarial examples, breaking cryptographic protections, and undermining AI model integrity. Additionally, the study examines different attack vectors, including quantum-enhanced adversarial perturbations, quantum machine learning (QML) vulnerabilities, and quantum decryption threats. The paper also discusses defensive strategies such as quantum-resistant AI models, quantum cryptographic defenses, and hybrid quantum-classical security frameworks that can mitigate these risks. By evaluating existing research and emerging trends, this review provides insights into the growing intersection of AI security and quantum computing. The findings emphasize the urgent need for robust quantum-aware AI security frameworks to safeguard AI-driven systems in the quantum era. Future research directions include developing quantum-adaptive AI models, post-quantum cryptographic techniques, and real-world applications of quantum-safe AI architectures. This review aims to contribute to the ongoing discourse on ensuring AI resilience against adversarial quantum threats, paving the way for a secure and quantum-resistant future.