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.
arXiv:2604.22903v2 Announce Type: replace Abstract: The integration of quantum machine learning with classical deep learning offers promising avenues for medical image analysis by mapping data into h...
arXiv:2608. 06846v1 Announce Type: cross Abstract: We test whether a parameterized quantum circuit (PQC) improves a hybrid quantum-classical model's performance on classical datasets, using an interface-matched classical map as the control while holding all other components fixed.
The paper introduces MMD-FUSE, a two-sample test that remains effective on small datasets by fusing classical and quantum kernels. By combining the inductive biases of classical kernels with the expressive power of quantum kernels, the hybrid approach achieves higher test power, especially for small, high‑dimensional data. Experiments on synthetic and real clinical datasets confirm that the method consistently outperforms purely classical counterparts and adapts robustly to varied data characteristics.
arXiv:2508. 19437v2 Announce Type: replace-cross Abstract: The importance of analyzing nontrivial datasets when testing quantum machine learning (QML) models is becoming increasingly prominent in literature, yet a cohesive framework for understanding dataset characteristics remains elusive.
The paper compares classical and hybrid quantum machine learning models for a trigger-like binary classification task using CMS open data. Eight classical models (SVM, ANN, CNN, LSTM) and eight quantum counterparts are evaluated under identical preprocessing, data splits, and decision thresholds, with performance measured by accuracy, ROC‑AUC, F1‑score, precision, and recall. The best classical model is an artificial neural network (93.53 % accuracy, 0.9819 ROC‑AUC), while the best quantum model is a quantum convolutional network (90.89 % accuracy, 0.9731 ROC‑AUC), indicating that within an eight‑qubit budget the quantum models do not surpass the classical ones.
arXiv:2608. 19304v1 Announce Type: cross Abstract: Lung cancer screening with low-dose chest computed tomography reduces mortality, but its impact is limited by uptake, adherence, and management challenges.