Hybrid Quantum-Classical ML

ML: Machine Learning

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8 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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HQ-UNet: A Hybrid Quantum-Classical U-Net with a Quantum Bottleneck for Remote Sensing Image Segmentation
Apr 29, 2026Md Aminur Hossain, Ayush V. Patel, Ikshwaku Vanani +1Remote Sensing Image Segmentation Hybrid Quantum-Classical ML
Space Applications Centre, Indian Space Research Organisation (ISRO), India · New-Brunswick Honors College, Rutgers University, New Jersey · Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay
On the Complementarity of Quantum and Classical Features: Adaptive Hybrid Quantum-Classical Feature Fusion for Breast Cancer Classification
Apr 24, 2026Yasmin Rodrigues Sobrinho, João Renato Ribeiro Manesco, João Paulo PapaHybrid Quantum-Classical ML Medical Image Classification
Department of Computing, São Paulo State University, Ave. Engenheiro Luiz Edmundo Carrijo Coube, 14-01 - Vargem Limpa, Bauru, 17033-360, São Paulo, Brazil
A Specialized Importance-Aware Quantum Convolutional Neural Network with Ring-Topology (IA-QCNN) for MGMT Promoter Methylation Prediction in Glioblastoma
Apr 24, 2026Emine Akpinar, Murat OduncuogluQuantum Convolutional Neural Networks Hybrid Quantum-Classical ML
Department of Physics, Yildiz Technical University, Istanbul, Turkey
Hybrid Quantum Neural Networks for Enhanced Breast Cancer Thermographic Classification: A Novel Quantum-Classical Integration Approach
Apr 18, 2026Riza Alaudin Syah, Irwan Alnarus Kautsar, Gunawan Witjaksono +1Hybrid Quantum-Classical ML Medical Image Classification
Faculty of Computing Universiti Teknologi Malaysia Johor Bahru, Malaysia · Informatics Department Faculty of Science and Technology Universitas Muhammadiyah Sidoarjo Sidoarjo, Indonesia · Department of Information System Universitas Siber Jakarta Jakarta, Indonesia
Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics
Apr 17, 2026Abhishek Sawaika, Durga Pritam Suggisetti, Udaya Parampalli +1Hybrid Quantum-Classical ML High-Energy Physics
qCLOUDS Lab, School of Computing and Information Systems, The University of Melbourne, Australia · Birla Institute of Technology and Science Pilani, Dubai, UAE
PINNACLE: An Open-Source Computational Framework for Classical and Quantum PINNs
Apr 17, 2026Shimon Pisnoy, Hemanth Chandravamsi, Ziv Chen +4Neural PDE Solvers PDE Surrogate Modeling
†Faculty of Mechanical Engineering, Technion Israel Institute of Technology, Haifa, Israel. · ‡Corresponding author. · §The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering, Technion Israel Institute of Technology, Haifa, Israel. +2
A Hybrid Quantum Circuit Born Machine Framework for Financial Volatility Forecasting: Quantum-Assisted Training and Classical Inference
Mar 10, 2026Yixiong ChenQuantitative Finance Quantum Machine Learning
Quantum Implicit Neural Representations for Novel View Synthesis
Jan 8, 2026Daniele Lizzio Bosco, Shuteng Wang, Giuseppe Serra +1Hybrid Quantum-Classical ML Novel View Synthesis
Department of Mathematics, Computer Science and Physics, University of Udine, Italy · Department of Biology, University of Naples Federico II, Italy · work done during an internship at the 4DQV research group, MPI for Informatics +1
Quantum Adaptive Self-Attention for Quantum Transformer Models
Apr 5, 2025Chi-Sheng Chen, En-Jui KuoHybrid Quantum-Classical ML Transformer Attention
Department of Electrophysics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
Classification of Financial Data Using Quantum Support Vector Machine
Dec 14, 2024Seemanta Bhattacharjee, MD. Muhtasim Fuad, A. K. M. Fakhrul HossainQuantitative Finance Hybrid Quantum-Classical ML
Shahjalal University Of Science and Technology, Sylhet-3114, Bangladesh
Classical and quantum kernel fusion for two-sample testing
Date pendingYu Terada, Yugo Ogio, Ken Arai +2Two-Sample Testing Maximum Mean Discrepancy
Advanced Research Laboratory, Sony Group Corporation, 1-7-1 Konan, Minato-ku, Tokyo 108-0075, Japan · Deloitte Tohmatsu Consulting LLC.
Trainability-Oriented Hybrid Quantum Regression via Geometric Preconditioning and Curriculum Optimization
Date pendingQingyu Meng, Yangshuai WangQuantum Machine Learning Hybrid Quantum-Classical ML
Shanghai Jiao Tong University-Chongqing Institute of Artificial Intelligence, Chongqing 401329, China. · Department of Mathematics, National University of Singapore, 10 Lower Kent Ridge Road, Singapore.

Apr 29, 2026, cs.CVJ/K move · Enter open · S save

HQ-UNet: A Hybrid Quantum-Classical U-Net with a Quantum Bottleneck for Remote Sensing Image Segmentation

Md Aminur Hossain, Ayush V. Patel, Ikshwaku Vanani, Biplab Banerjee

Space Applications Centre, Indian Space Research Organisation (ISRO), India · New-Brunswick Honors College, Rutgers University, New Jersey · Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay

Remote Sensing Image Segmentation Hybrid Quantum-Classical ML Quantum Neural Networks

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Semantic segmentation in remote sensing is commonly addressed using classical deep learning architectures such as U-Net, which require a large number of parameters to model complex spatial relationships. Quantum machine learning (QML) provides an alternative representation paradigm by mapping classical features into quantum states, but its direct application to high-dimensional images remains challenging under near-term quantum hardware constraints. In this work, we propose HQ-UNet, a hybrid quantum-classical U-Net architecture that integrates a compact parameterized quantum circuit at the bottleneck of a classical U-Net. The proposed design uses a non-pooling quantum convolutional module to enrich highly compressed encoder features before decoding, while keeping the quantum component shallow and parameter-efficient. Experiments on the LandCover.ai dataset show that HQ-UNet achieves a mean IoU of 0.8050 and an overall accuracy of 94.76%, outperforming the classical U-Net baseline. These results suggest that compact quantum bottlenecks can enhance feature representation for remote sensing image segmentation under near-term quantum constraints. This highlights the potential of hybrid quantum-classical designs as a promising direction for parameter-efficient dense prediction in Earth observation.

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