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.
By Utsav Poudel, Anuj Nepal, Vyas Viswanath, Subramaniyaswamy Vairavasundaram
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...
By Anand Kumar Mishra, Ramanuj Awasthi
arXiv:2606. 14194v1 Announce Type: cross Abstract: This paper presents a two-stage Hybrid Classical-Quantum (HCQ) pipeline for binary Alzheimer's disease (AD) classification from 3D T1-weighted structural MRI volumes, where the classical and quantum components are designed to complement each other rather than operate independently.
By Tia Tiwari, Vamshi Krishna Kancharla, Neelam Sinha
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.
By Alona Sakhnenko, Christian B. Mendl, Jeanette M. Lorenz
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.
By Tariq Mahmood, Muhammad Awais Rafique, Talab Hussain, Juan Pablo Perez Aguilar, Alfredo Raya, Muhammad Ahsan
arXiv:2601.04732v2 Announce Type: replace-cross
Abstract: Quantum machine learning has emerged as a promising application domain for near-term quantum hardware, particularly through hybrid quantum-cl...
By Dominik Freinberger, Philipp Moser
arXiv:2607. 22516v1 Announce Type: cross Abstract: A central design principle in modern machine learning and artificial intelligence is to align a model's inductive bias with the structure of its input data.
By Peiyong Wang, Udaya Parampalli, Casey R. Myers
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.
By Azadeh Alavi, Hamidreza Khalili, Stanley H. Chan, Fatemeh Kouchmeshki, Muhammad Usman, Ross Vlahos
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.
By Hao-Yuan Chen
arXiv:2605. 27923v2 Announce Type: replace-cross Abstract: The rapid growth of computer vision and increasingly complex image recognition tasks has exposed fundamental computational limitations of classical machine learning models, motivating the exploration of quantum computing as an emerging new paradigm.
By Sudip Vhaduri, Ryan Gammon, Sayanton Dibbo
The paper introduces Neural Fourier Surrogates (NFS), a stochastic classical neural network that learns coefficients over a finite Fourier series support similar to quantum neural networks (QNNs). By testing on tabular benchmark datasets, NFS demonstrates competitive classification performance against established classical baselines and data‑reuploading QNNs. The authors also compare the learned Fourier spectra of QNNs and NFS on synthetic data, positioning NFS as a natural classical baseline for evaluating QNN performance.
By Oliver Knitter, Jonathan Mei, Sang Hyub Kim, Chi Chen, Masako Yamada, Martin Roetteler
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.
By Yu Terada, Yugo Ogio, Ken Arai, Hiroyuki Tezuka, Yu Tanaka