Quantum Convolutional Neural Networks

Also known as QCNN

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Volcanic Clouds Detection through QCNN and Geostationary Satellite Multispectral Imagery
Jul 29, 2026Federica Torrisi, Claudia Corradino, Alessandro Grilli +5Remote Sensing Image Understanding Quantum Convolutional Neural Networks
Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione Osservatorio Etneo · Thales Alenia Space Italia · Dipartimento di Fisica e Astronomia “Ettore Majorana,” Universit`a di Catania, Catania, Italy +2
Do emulated quantum circuits change what CNNs look at? Performance and explainability comparison in medical image classification
Jul 23, 2026Guillermo Rubiños Rodríguez, Martín Ottavianelli, Mateo Alonso +4Quantum Convolutional Neural Networks Quantum-Inspired ML
Instituto Tecnol´ogico de Galicia, Cant´on Grande 9, Planta 3, 15003 A Coru˜na, Spain · Centro Singular de Investigaci´on en Tecnolox´ıas Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Santiago de Compostela, 15782, Spain · Department of Neuroradiology, LMU University Hospital, Ludwig Maximilian University of Munich, Marchioninistraße 15, 81377 Munich, Germany +1
A Novel Parallel QCNN Architecture with Efficient Classical Simulability
Jul 9, 2026Lawrence Nguyen, Hiu Yung WongQuantum Convolutional Neural Networks Quantum Circuit Simulation
Department of Electrical Engineering San Jos´e State University San Jos´e, USA
QCNN with Rough Path Signature Kernels
Jul 8, 2026Leonardo Nogueira Falabella, Vasily SazonovTime Series Classification Quantum Convolutional Neural Networks
Université Paris-Saclay, CEA, List, F-91120, Palaiseau, France
Low-Overhead Error-Corrected QCNNs Using Bivariate Bicycle Codes
Jul 7, 2026Alejandro Rosales, Animesh YadavQuantum Convolutional Neural Networks Quantum Error Correction
School of Electrical Engineering and Computer Science Ohio University, Athens, OH, USA
Quantum Convolutional Neural Networks for Groundwater Heat Plume Prediction: A Surrogate Modeling Approach
Jun 22, 2026Danyal Maheshwari, Julia Pelzer, Miriam SchulteNeural Surrogate Modeling Quantum Convolutional Neural Networks
Simulation of Large Systems, Institute for Parallel and Distributed Systems (IPVS),, University of Stuttgart, Stuttgart, Germany
Quantum Machine Learning for Industrial Applications
Jun 12, 2026Léo MonbroussouQuantum Convolutional Neural Networks Quantum Machine Learning
Sorbonne Université, Ecole Doctorale Informatique, Télécommunications et Electronique (n° 572), Computer Science
Do We Really Need Quantum Machine Learning?: A Multidimensional Empirical Study
May 27, 2026Sudip Vhaduri, Ryan Gammon, Sayanton DibboQuantum Convolutional Neural Networks Quantum Machine Learning
Department of Computer Science, University of Alabama, AL 35487 · Clemson University
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

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

Volcanic Clouds Detection through QCNN and Geostationary Satellite Multispectral Imagery

Federica Torrisi, Claudia Corradino, Alessandro Grilli, Tommaso Catuogno+4

Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione Osservatorio Etneo · Thales Alenia Space Italia · Dipartimento di Fisica e Astronomia “Ettore Majorana,” Universit`a di Catania, Catania, Italy+2

Remote Sensing Image Understanding Quantum Convolutional Neural Networks Hybrid Quantum-Classical ML Remote Sensing Quantum Neural Networks

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Recent advances in quantum computing are opening new possibilities for Earth Observation (EO) data analysis. Quantum machine learning (QML) approaches offer novel ways to process information by exploiting quantum phenomena such as superposition and entanglement. These capabilities have motivated the exploration of whether quantum-enhanced models can address long-standing challenges in satellite remote sensing, where complex spectral and spatial signals often require sophisticated feature extraction. Among various fields of application, EO data allow the global monitoring of volcanic clouds and are crucial for aviation safety, hazard assessment, real-time eruption response, and evaluation of volcanic impacts on climate. Yet accurate detection of volcanic clouds remains difficult due to their similarity with meteorological clouds, the variability of eruption signatures, and the coarse spectral sampling of geostationary sensors. In this work, the potential of hybrid quantum convolutional neural networks (QCNNs) for the classification of satellite images containing volcanic clouds was investigated. These architectures integrate quantum computational layers into a classical convolutional framework. Two QCNN variants (with 2 and 4 qubits) have been considered to evaluate their ability to classify a dataset of SEVIRI images, including scenes with volcanic clouds (composed of ash,

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, or mixed components) as well as non-volcanic backgrounds. Finally, the performance of the hybrid QCNN models was compared with that of purely classical architectures.

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