arXiv:2609.05702v2 Announce Type: cross Abstract: Quantum Machine Learning (QML) has shown rapid advances by utilizing quantum computing for machine learning tasks. Meanwhile, the privacy risks accom...
arXiv:2609.25082v1 Announce Type: new Abstract: Quantum machine learning (QML) is increasingly recognized as one of the most promising near-term applications of quantum computing, viewed as a next-fr...
The study evaluates quantum machine learning (QML) models for network intrusion detection against well-tuned classical baselines across four standard datasets, using a leakage-controlled protocol and noise simulation. It introduces a quantum-attribution audit to determine whether any performance gains are truly due to quantum effects. While most tuned classical models match or surpass QML, two quantum approaches— a quantum-kernel SVM and a small hybrid circuit—show statistically significant advantages on specific metrics and tasks.
arXiv:2608. 13914v1 Announce Type: cross Abstract: Electrocardiogram (ECG) recordings are sensitive biomedical data, limiting the ability of hospitals and wearable devices to share raw signals for centralized model training.
arXiv:2602. 01177v3 Announce Type: replace-cross Abstract: We develop an information-theoretic framework connecting stability, privacy, and generalization for quantum learning algorithms.
The paper demonstrates that publicly available clinical machine learning models, such as logistic regression (LR), pose significant privacy risks because attackers can recover model parameters and identify training cohorts through various membership inference attacks. The authors show that even small cohorts can be reliably identified and that common practices like cross-validation can worsen the risk. To mitigate this, they propose a quantum-inspired defense that tensorizes discretized models into tensor trains (TTs), which obfuscates parameters, preserves accuracy, and maintains interpretability while providing black‑box protection comparable to Differential Privacy.
arXiv:2609.23476v1 Announce Type: cross Abstract: A potential path forward is Quantum Machine Learning (QML), which aims to leverage quantum computing in conjunction with classical machine learning t...
Privacy-preserving clustering is critical for analyzing sensitive data in healthcare, cybersecurity, and enterprise applications, where maintaining data confidentiality must be balanced with analytical performance. This paper presents Equivariant Quantum Clustering (EQC), a parameter-efficient framework that integrates symmetry-aware quantum circuits with differential privacy to improve the privacy-utility tradeoff.
The study evaluates quantum kernel methods for classification against tuned classical kernels, addressing inconsistencies in prior benchmarks by implementing protocol controls such as balanced bandwidth tuning, fold‑specific kernel recomputation, and hardware‑based cost measurement. A factorial design across 324 experimental cells—varying dimensionality, training size, class separability, and data structure—reveals that optimizing the quantum feature map’s bandwidth yields the largest accuracy gains, surpassing any quantum‑classical performance differences observed. Consequently, tuned quantum kernels are statistically indistinguishable from tuned RBF kernels on four real datasets, and the apparent advantages on synthetic data diminish with increasing dimensionality and are largely attributable to protocol variance rather than inherent quantum benefits.
arXiv:2605.25066v2 Announce Type: replace-cross Abstract: As machine-learning workloads move onto quantum hardware they inherit a trust problem classical AI deployments have largely solved and quantu...
arXiv:2602. 14735v2 Announce Type: replace-cross Abstract: The performance of quantum classifiers is typically analyzed through global state distinguishability or the trainability of variational models.
arXiv:2606. 31536v1 Announce Type: new Abstract: As Quantum Machine Learning (QML) transitions toward practical implementation, the field faces a critical architectural bottleneck that challenges the fundamental assumptions of classical statistical learning theory.