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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...
arXiv:2601. 00921v3 Announce Type: replace-cross Abstract: Chronic obstructive pulmonary disease (COPD) affects hundreds of millions of people worldwide, and skeletal-muscle dysfunction is clinically important.
The study compared six families of quantum machine learning models with classical counterparts using identical correlation-derived features across four large neuroimaging cohorts. Across all cohorts, quantum kernels performed statistically indistinguishable from matched classical kernels, with average accuracy differences within 1.4 percentage points. While a hybrid quantum convolutional network matched classical accuracy and a trainable quantum kernel slightly improved AUC on ABIDE I, overall quantum models did not consistently outperform classical ones, providing a feature‑matched benchmark of 284 comparisons.
Resting-state functional connectivity is widely used in machine learning-based classification of neuropsychiatric disorders. Quantum machine learning models have been proposed for functional connectiv...
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.
The study audits fairness in quantum and classical machine‑learning classifiers for medical imaging, focusing on dermatological lesions (Fitzpatrick skin type) and chest X‑rays (age group). Using ResNet‑18 embeddings, the authors train SVM, MLP, QSVM, and QCNN under baseline and mitigation strategies (Equalized Odds regularisation, Gradient Reversal Learning, and both combined). Results show that while quantum models can match aggregate performance, they often exhibit subgroup disparities, and some mitigation approaches reduce fairness without maintaining clinical performance.