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.
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.
Quantum machine learning (QML) is emerging as a promising paradigm at the intersection of quantum computing and artificial intelligence, yet its security under adversarial conditions remains insufficiently understood. This scoping review aims to systematically map empirical research on adversarial robustness in QML and to identify dominant threat models, defense strategies, evaluation approaches, practical constraints, and future research directions. Following PRISMA-ScR guidelines, four major databases were searched, resulting in 53 eligible empirical studies published between 2020 and 2026. The findings show that most research concentrates on input-level evasion attacks, particularly adversarial examples, and primarily evaluates robustness in classification-oriented models such as variational quantum circuits and quantum neural networks. Defense strategies are largely adapted from classical adversarial training and noise-based mitigation, with limited deployment on real quantum hardware. Robustness assessment is predominantly empirical, relying on accuracy degradation and attack success rate, while formal certification methods remain less common. The literature also highlights substantial constraints related to hardware limitations, NISQ noise, computational cost, and dataset scale. Overall, the evidence indicates that adversarial robustness research in QML is expanding but remains methodologically concentrated, underscoring the need for standardized benchmarking, scalable defenses, and hardware-validated robustness evaluation frameworks.
Quantum Machine Learning (QML), a confluence of quantum computing and classical machine learning, represents a revolutionary paradigm with transformative potential. This systematic review explores the landscape of QML by investigating its underlying principles, methodologies, diverse applications, challenges, and ethical considerations. Beginning with an examination of fundamental quantum computing principles, the review navigates through various QML methodologies, comparing them with classical counterparts. Real-world applications, ranging from quantum-enhanced optimization to drug discovery, are scrutinized, showcasing the practical implications of QML across industries. The paper systematically identifies challenges, including quantum hardware constraints and ethical considerations, while offering insights into current limitations and future research directions. A comparative analysis benchmarks QML against classical machine learning approaches, providing a nuanced understanding of its strengths and limitations. Ethical considerations underscore the importance of responsible AI practices in the integration of QML. The review concludes by identifying research gaps and suggesting future directions, emphasizing the need for continued exploration in this dynamic intersection of quantum computing and machine learning. This comprehensive exploration serves as a valuable resource for researchers, practitioners, and decision-makers seeking insights into the current state and transformative potential of Quantum 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.
This project presents a scoping review titled Adversarial Robustness in Quantum Machine Learning: A Scoping Review. The purpose of the study is to systematically map and synthesize empirical research on adversarial robustness in quantum machine learning (QML), an emerging field at the intersection of quantum computing and artificial intelligence. As QML systems move closer to real-world deployment, understanding their vulnerability to adversarial attacks and the effectiveness of proposed defenses becomes critical for ensuring reliability and security. Following the PRISMA-ScR framework, this review analyzes 53 empirical studies published between 2020 and 2026 across major databases including Scopus, ACM, IEEE Xplore, and MDPI. The study examines six key dimensions: (1) adversarial threat vectors investigated in QML, (2) QML model families evaluated under attack, (3) defense strategies proposed to enhance robustness, (4) robustness evaluation methods employed, (5) practical and technological constraints influencing research, and (6) future research directions identified in the literature. Expected outcomes include a structured taxonomy of adversarial threats in QML, a comparative mapping of defense mechanisms, identification of methodological gaps, and clarification of hardware and scalability constraints affecting robustness research. The review aims to provide a consolidated evidence base to guide future research, support the development of standardized benchmarking practices, and inform the design of more secure and scalable QML systems suitable for deployment in high-risk domains such as cybersecurity and healthcare.
Quantum Machine Learning (QML) has emerged as a promising paradigm that combines the power of quantum computing with the adaptability of machine learning. QML models have demonstrated potential advantages in solving complex problems across domains such as optimization, pattern recognition, and generative modeling. While much attention has been devoted to exploring their computational benefits and capabilities, the security of QML models and their robustness to adversarial attacks remains an underexplored area in the literature. Understanding adversarial vulnerabilities is critical for assessing the reliability of QML systems, especially as they transition from theoretical constructs to practical applications. In this work, we present an empirical study on the adversarial robustness of QML models. We systematically evaluate the susceptibility of quantum models to adversarial attacks, comparing them with classical counterparts across different settings. Additionally, we examine cross-paradigm attack transferability by testing classical attacks on quantum models and vice versa. Our results indicate that QML models exhibit higher vulnerability in white-box settings and stronger attack transferability than classical ML models. Furthermore, while adversarial training can provide some robustness, its effectiveness remains limited. Our findings contribute to a better understanding of QML security and provide insights into the challenges of building robust QML systems.
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.