By leveraging the principles of quantum mechanics, QML opens doors to novel approaches in machine learning and offers potential speedup. However, machine learning models are well documented to be vulnerable to malicious manipulations, and this susceptibility extends to the models of QML. This situation necessitates a thorough understanding of QML's resilience against adversarial attacks, particularly in an era where quantum computing capabilities are expanding. In this regard, this paper examines model-independent bounds on adversarial performance for QML. To the best of our knowledge, we introduce the first computation of an approximate lower bound for adversarial error when evaluating model resilience against sophisticated quantum-based adversarial attacks. Experimental results are compared to the computed bound, demonstrating the potential of QML models to achieve high robustness. In the best case, the experimental error is only 10% above the estimated bound, offering evidence of the inherent robustness of quantum models. This work not only advances our theoretical understanding of quantum model resilience but also provides a precise reference bound for the future development of robust QML algorithms.
By leveraging the principles of quantum mechanics, QML opens doors to novel approaches in machine learning and offers potential speedup. However, machine learning models are well documented to be vulnerable to malicious manipulations, and this susceptibility extends to the models of QML. This situation necessitates a thorough understanding of QML's resilience against adversarial attacks, particularly in an era where quantum computing capabilities are expanding. In this regard, this paper examines model-independent bounds on adversarial performance for QML. To the best of our knowledge, we introduce the first computation of an approximate lower bound for adversarial error when evaluating model resilience against sophisticated quantum-based adversarial attacks. Experimental results are compared to the computed bound, demonstrating the potential of QML models to achieve high robustness. In the best case, the experimental error is only 10% above the estimated bound, offering evidence of the inherent robustness of quantum models. This work not only advances our theoretical understanding of quantum model resilience but also provides a precise reference bound for the future development of robust QML algorithms.
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.
As with classical neural networks, quantum machine learning (QML) models are vulnerable to small input perturbations that can significantly alter output predictions. Certifying the robustness of QML models, particularly on NISQ hardware, is therefore a fundamental step toward trustworthy quantum AI. This chapter reviews our recently developed comprehensive formal framework for verifying adversarial robustness in QML. The core of this framework is a fidelity-based robustness lower bound computable directly from the measurement outcome distribution, which enables both formal verification and empirical estimation on real quantum devices. Additionally, the optimal bound can be computed via semidefinite programming (SDP) with full knowledge of the quantum machine learning models. We incorporate these results into: (1) an efficient formal verification framework; (2) VeriQR, the first dedicated QML robustness verification tool; and (3) the first experimental benchmark of quantum adversarial robustness on a 20-qubit superconducting processor. Together, these systematic advances enable scalable, physically grounded robustness evaluation of QML models.
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.
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.
Abstract Despite their ever more widespread deployment throughout society, machine learning algorithms remain critically vulnerable to being spoofed by subtle adversarial tampering with their input data. The prospect of near-term quantum computers being capable of running quantum machine learning (QML) algorithms has therefore generated intense interest in their adversarial vulnerability. Here we show that quantum properties of QML algorithms can confer fundamental protections against such attacks, in certain scenarios guaranteeing robustness against classically-armed adversaries. We leverage tools from many-body physics to identify the quantum sources of this protection. Our results offer a theoretical underpinning of recent evidence which suggest quantum advantages in the search for adversarial robustness. In particular, we prove that quantum classifiers are: (i) protected against weak perturbations of data drawn from the trained distribution, (ii) protected against local attacks if they are insufficiently scrambling, and (iii) show evidence that they are protected against universal adversarial attacks if they are sufficiently chaotic. Our analytic results are supported by numerical evidence demonstrating the applicability of our theorems and the resulting robustness of a quantum classifier in practice. This line of inquiry constitutes a concrete pathway to advantage in QML, orthogonal to the usually sought improvements in model speed or accuracy.
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.