With the growing interest in Quantum Machine Learning (QML) and the increasing availability of quantum computers through cloud providers, addressing the potential security risks associated with QML has become an urgent priority. One key concern in the QML domain is the threat of data poisoning attacks in the current quantum cloud setting. Adversarial access to training data could severely compromise the integrity and availability of QML models. Classical data poisoning techniques require significant knowledge and training to generate poisoned data, and lack noise resilience, making them ineffective for QML models in the Noisy Intermediate Scale Quantum (NISQ) era. In this work, we first propose a simple yet effective technique to measure intra-class encoder state similarity (ESS) by analyzing the outputs of encoding circuits. Leveraging this approach, we introduce a \underline{Qu}antum \underline{I}ndiscriminate \underline{D}ata Poisoning attack, QUID. Through extensive experiments conducted in both noiseless and noisy environments (e.g., IBM\_Brisbane's noise), across various architectures and datasets, QUID achieves up to
92% accuracy degradation in model performance compared to baseline models and up to
75% accuracy degradation compared to random label-flipping. We also tested QUID against state-of-the-art classical defenses, with accuracy degradation still exceeding
50%, demonstrating its effectiveness. This work represents the first attempt to reevaluate data poisoning attacks in the context of QML.
The integration of quantum machine learning (QML) within open radio access network (O-RAN) architectures presents a promising frontier for enhancing interference classification and optimizing various 6G network use cases. We introduce a novel hybrid quantum-classical neural network (HQCNN) design that addresses critical challenges in noisy intermediate-scale quantum (NISQ) environments through an innovative feature preprocessing pipeline. By integrating deep convolutional feature extraction via ResNet-18 with principal component analysis (PCA)-based dimensionality reduction, we develop a robust computational framework that mitigates quantum gate errors and enhances computational stability. However, despite these advancements, HQCNNs remain vulnerable to adversarial attacks. Traditional classical backdoor methods are ineffective in the quantum setting due to NISQ noise and gate synthesis complexities, while existing quantum attack techniques suffer from a lack of selectivity and adaptability, making them easy to detect. To address this, we introduce Q-Poison, a novel stealthy attack that leverages parameterized quantum gates to significantly enhance attack success rates. This attack induces systematic misclassification of trigger inputs while maintaining 54.5% clean data accuracy, compared to 78.5% without attack. Our results demonstrate a substantial 1.44-fold reduction in accuracy, highlighting the need for robust defense mechanisms in quantum-enabled networks.
Anomaly Detection (AD) is critical in data analysis, particularly within the domain of IT security. In this study, we explore the potential of Quantum Machine Learning for application to AD with special focus on the robustness to noise and adversarial attacks. We build upon previous work on Quantum Support Vector Regression (QSVR) for semisupervised AD by conducting a comprehensive benchmark on IBM quantum hardware using eleven datasets. Our results demonstrate that QSVR achieves strong classification performance and even outperforms the noiseless simulation on two of these datasets. Moreover, we investigate the influence of - in the NISQ-era inevitable - quantum noise on the performance of the QSVR. Our findings reveal that the model exhibits robustness to depolarizing, phase damping, phase flip, and bit flip noise, while amplitude damping and miscalibration noise prove to be more disruptive. Finally, we explore the domain of Quantum Adversarial Machine Learning by demonstrating that QSVR is highly vulnerable to adversarial attacks, with neither quantum noise nor adversarial training improving the model's robustness against such attacks.