arXiv:2606. 03517v1 Announce Type: cross Abstract: Training quantum neural networks (QNNs) on quantum hardware is currently bottlenecked by the cost of gradient estimation: standard parameter-shift methods require a number of circuit evaluations that grows quadratically with the number of trainable parameters, making hardware-based optimisation impractical beyond small system sizes.
By Natansh Mathur, Panagiotis Kl. Barkoutsos, Masako Yamada, Martin Roetteler, Iordanis Kerenidis
arXiv:2607. 09113v1 Announce Type: cross Abstract: Data scarcity and class imbalance are persistent challenges in machine learning that degrade model generalization and introduce predictive bias.
By Tanapol Nuatho, Narisorn Sangnakara, Prapong Prechaprapranwong, Rajchawit Sarochawikasit
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
Missing data are a fundamental challenge in statistical analysis and machine learning, as the choice of imputation method substantially impacts downstream inference. In this work, we propose two hybri...
The paper proposes Entanglement-Weighted Pruning (EWP), a method for unlearning a client’s contribution from a federated quantum classifier without retraining from scratch. EWP scores each trainable circuit parameter by combining a Fisher‑information estimate on the target client’s data with a structural entanglement weight, pruning the lowest‑scoring parameters and optionally fine‑tuning the remaining ones. Experiments on a four‑qubit data‑re‑uploading ansatz trained with FedAvg across five simulated supply‑chain‑risk clients show that EWP achieves accuracy comparable to full retraining while reducing forgetting and wall‑clock time by about sixteenfold, outperforming random, Fisher‑only, or entanglement‑only pruning.
By Aditya Kumar, Sumit Chongder
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: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...
By Yasmin Rodrigues Sobrinho, Jo\~ao Renato Ribeiro Manesco, Jo\~ao Paulo Papa
arXiv:2611.05442v1 Announce Type: new
Abstract: Background: Quantum machine learning (QML) is promoted for clinical prediction, but comparisons with classical models are rarely paired or tested for e...
By Mohammed Siraj B, Gousia Habib, Ruban S
The paper introduces two hybrid imputation methods, NuclearForest and SoftForest, that combine a low‑rank initialization (using Singular Value Thresholding or SoftImpute) with a single Random Forest refinement. These methods capture global covariance patterns and local nonlinear dependencies, respectively, and are benchmarked against seven existing techniques across MCAR, MAR, and MNAR scenarios. Results show comparable or superior imputation accuracy to state‑of‑the‑art iterative methods while achieving significant speedups of roughly 5.8× and 9.5× over MissForest.
By Jinwei Li, Michelle Bruch, Daniel Tenbrinck
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
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:2609.01537v1 Announce Type: new
Abstract: Q-matrices play a central role in cognitive diagnosis within educational data mining (EDM), specifying which latent skills each assessment item require...
By Arif Hassan Zidan, Yi Pan, Bowen Guo, Xiang Li, Yu Bao, Yingfeng Wang, Tianming Liu, Wei Zhang