Quantum computing has been a major priority for several nations and prominent institutions in their pursuit of a transformative breakthrough in the fields of computation and encryption. By using the principles of quantum mechanics, particularly quantum superposition and entanglement, quantum computing and quantum machine learning (QML) have the potential to enhance artificial intelligence (AI) and achieve quantum supremacy with unprecedented computational power. However, despite its exceptional learning capabilities, QML-based applications face several emerging security threats. Unlike previous studies focused on classical quantum cryptography and secure quantum communications, this work investigates adversarial risks in QML-assisted network functions and digital twin applications. Specifically, we highlight vulnerabilities such as quantum kernel poisoning, backdoor attacks, and adversarial noise. Key findings reveal that adversaries can intercept quantum states in transit, manipulate parameterized quantum circuits (PQCs), and exploit variational quantum algorithms (VQAs) through adversarial qubit perturbations. These attacks can mislead QML-based optimization processes, leading to incorrect digital twin predictions, faulty resource allocation, or disruptions in QML-aided network functions. To mitigate these risks, defense strategies such as quantum-safe cryptography, data sanitization, adversarial training, defensive distillation, and gradient masking in quantum circuit design can be employed. However, the key issue is the absence of robust security solutions for real-world deployment. Future research should examine the trade-off between adversarial robustness and generative learning performance. Key areas include quantum state discrimination, secure quantum federated learning, quantum decoherence control, and secure quantum semantic communications for real-world deployment.
Quantum computing has been a major priority for several nations and prominent institutions in their pursuit of a transformative breakthrough in the fields of computation and encryption. By using the principles of quantum mechanics, particularly quantum superposition and entanglement, quantum computing and quantum machine learning (QML) have the potential to enhance artificial intelligence (AI) and achieve quantum supremacy with unprecedented computational power. However, despite its exceptional learning capabilities, QML-based applications face several emerging security threats. Unlike previous studies focused on classical quantum cryptography and secure quantum communications, this work investigates adversarial risks in QML-assisted network functions and digital twin applications. Specifically, we highlight vulnerabilities such as quantum kernel poisoning, backdoor attacks, and adversarial noise. Key findings reveal that adversaries can intercept quantum states in transit, manipulate parameterized quantum circuits (PQCs), and exploit variational quantum algorithms (VQAs) through adversarial qubit perturbations. These attacks can mislead QML-based optimization processes, leading to incorrect digital twin predictions, faulty resource allocation, or disruptions in QML-aided network functions. To mitigate these risks, defense strategies such as quantum-safe cryptography, data sanitization, adversarial training, defensive distillation, and gradient masking in quantum circuit design can be employed. However, the key issue is the absence of robust security solutions for real-world deployment. Future research should examine the trade-off between adversarial robustness and generative learning performance. Key areas include quantum state discrimination, secure quantum federated learning, quantum decoherence control, and secure quantum semantic communications for real-world deployment.
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.
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.
Quantum Machine Learning (QML) fuses quantum computing with machine learning to address complex tasks with potential speedups over classical methods. While QML models such as variational quantum classifiers (QVC), quantum multilayer perceptron(QMLP) models and quantum convolutional neural networks (QCNN) have demonstrated promise in applications like image recognition and malware classification, they remain vulnerable to adversarial threats. Inspired by the vulnerabilities in classical ML, this work systematically investigates how attacks-ranging from training-time poisoning and inferencetime evasion (e.g., quantum versions of FGSM and PGD) to deployment-time trigger attacks-manifest in quantum circuits. We explore how classical adversarial strategies adapt to quantum models through parameterized encodings, hybrid architectures, and probabilistic outputs. The poster evaluates different quantum classifiers under adversarial settings, comparing their robustness, trainability, and resilience to perturbations. Defense mechanisms such as randomized encoding, engineered noise channels, and quantum adversarial training are analyzed in relation to specific threat types. In addition, we assess how quantum models behave differently than classical counterparts under attack, and how quantum-specific design constraints can either exacerbate or mitigate these effects. Our ultimate goal is to define and construct robust QML systems that are both theoretically sound and practically secure. By synthesizing results across multiple QML architectures and threat models, this work provides actionable insight into the development of resilient quantum classifiers, paving the way toward trustworthy and scalable quantum machine learning.
Artificial Intelligence has achieved remarkable success across diverse application domains. However, its vulnerability to adversarial attacks poses significant challenges to reliability, security, and trustworthiness. Adversarial machine learning demonstrates that even highly accurate models can be manipulated through carefully crafted perturbations, raising serious concerns in safety critical systems such as healthcare, finance, and autonomous technologies. In parallel, quantum computing has emerged as a transformative paradigm capable of addressing complex computational problems through principles such as superposition, entanglement, and quantum interference. The convergence of these fields has led to the emergence of quantum artificial intelligence, which explores how quantum techniques can enhance learning efficiency, scalability, and robustness. This chapter provides a comprehensive overview of adversarial machine learning and existing defense strategies, followed by an accessible introduction to quantum computing and quantum machine learning models. It further presents conceptual frameworks for quantum-enhanced adversarial robustness, emphasizing quantum optimization, feature mapping, and hybrid quantum classical architectures. Practical applications, key challenges, and future research directions are also discussed to support the development of secure and trustworthy AI systems.
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.
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.