This paper examines the adversarial robustness of Quantum Neural Networks (QNNs) compared to classical AI models. Using FGSM and PGD attacks, it evaluates how quantum features such as entanglement and superposition influence resilience to perturbations. Results show that while QNNs display partial robustness, especially under iterative attacks, they remain limited by current simulation constraints. The work highlights both the promise and challenges of applying quantum principles to strengthen machine-learning security.
This paper examines the adversarial robustness of Quantum Neural Networks (QNNs) compared to classical AI models. Using FGSM and PGD attacks, it evaluates how quantum features such as entanglement and superposition influence resilience to perturbations. Results show that while QNNs display partial robustness, especially under iterative attacks, they remain limited by current simulation constraints. The work highlights both the promise and challenges of applying quantum principles to strengthen machine-learning security.
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.
Quantum machine learning, noted for its remarkable advancements in enhancing computational speed and augmenting data processing efficacy, is acquiring considerable recognition within the scientific community. Despite the noteworthy advancements of quantum machine learning, it shares with its traditional counterpart a susceptibility to adversarial threats. This presents a significant challenge and underscores the need for robust countermeasures in this emerging field of study. Developing effective adversarial attack algorithms, such as the Quantum Fast Gradient Sign Method (QFGSM), is crucial to opening the path for exploring robust defense mechanisms against these threats. However, unlike traditional machine learning, the quantum field currently lacks the effective techniques to orchestrate adversarial attacks. This study introduces and assesses the QFGSM, a adversarial attack algorithm tailored for QNNs. Additionally, we evaluate the effect of random noise and its quantum version on these models, providing a comprehensive comparison to understand the efficacy of adversarial attack methods. The evaluation is conducted on both traditional Neural Networks (NNs) and QNNs using the ClaMP dataset, a cybersecurity-focused dataset, and involves performance metrics like Accuracy, Precision, Recall, and F1 score. Our findings underscore the differential resilience of NNs and QNNs under adversarial attacks and reveal the contrasting effects of FGSM, QFGSM, and random noise-based methods. Our study reveals that Quantum Neural Networks (QNNs) show significant vulnerability to our proposed Quantum Fast Gradient Sign Method (QFGSM) compared to random noise, indicating a need for quantum-specific defenses in the face of this advanced adversarial attack algorithm.
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.
Machine learning can thrust technological advances and benefit different application areas. Further, with the rise of quantum computing, machine learning algorithms have begun to be implemented in a quantum environment; this is now referred to as quantum machine learning. There are several attempts to implement deep learning in quantum computers. Nevertheless, they were not entirely successful. Then, a convolutional neural network (CNN) combined with an additional quanvolutional layer was discovered and called a quanvolutional neural network (QNN). A QNN has shown a higher performance over a classical CNN. As a result, QNNs could achieve better accuracy and loss values than the classical ones and show their robustness against adversarial examples generated from their classical versions. This work aims to evaluate the accuracy, loss values, and adversarial robustness of QNNs compared to CNNs.
Abstract The rapid advancements in machine learning and quantum computing have given rise to a new research frontier: quantum machine learning. Quantum models designed for tackling classification problems possess the potential to deliver speed enhancements and superior predictive accuracy compared to their classical counterparts. However, recent research has revealed that quantum neural networks (QNNs), akin to their classical deep neural network-based classifier counterparts, are vulnerable to adversarial attacks. In these attacks, meticulously designed perturbations added to clean input data can result in QNNs producing incorrect predictions with high confidence. To mitigate this issue, we suggest enhancing the adversarial robustness of quantum machine learning systems by incorporating noise layers into QNNs. This is accomplished by solving a Min-Max optimization problem to control the magnitude of the noise, thereby increasing the QNN’s resilience against adversarial attacks. Extensive numerical experiments illustrate that our proposed method outperforms state-of-the-art defense techniques in terms of both clean and robust accuracy.
Quantum Machine Learning (QML) has recently emerged as a highly promising research frontier. Within this domain, Quantum Neural Networks (QNNs),characterized by Variational Quantum Circuits (VQCs) at their core and featuring layers of quantum gates optimized by classical algorithms, have garnered significant attention. However, a rigorous and exhaustive evaluation of their practical performance remains largely incomplete. In this study, we conduct a comprehensive comparative analysis of three prominent hybrid classical-quantum architectures: Quantum Convolutional Neural Networks (QCNN), Quantum Recurrent Neural Networks (QRNN), and Quantum Vision Transformers (QViT), focusing on the critical dimensions of generalization, accuracy, and robustness. Our findings provide novel insights that address previous evaluative gaps. Notably, while these models exhibit exceptional performance on low-feature datasets such as MNIST, their learning efficacy degrades significantly when transitioned to high-feature datasets. Furthermore, convolutional-based models like QCNN appear less effective on high-dimensional data than other machine learning architectures. Additionally, while all models are susceptible to adversarial noise, traditional architectures, such as recurrent and convolutional networks, demonstrate superior resilience. Conversely, in the presence of quantum noise, the transformer-based architecture proves its strength by maintaining high robustness against measurement noise, channel noise, and finite-shot effects, whereas other architectures suffer marked performance declines. These results provide a granular perspective on the current state of the field and underscore the critical importance of tailoring model selection to the constraints of contemporary Noisy Intermediate-Scale Quantum (NISQ) environments.