Quantum Neural Networks

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Quantum neural networks study how quantum states, gates, and measurements can be trained to perform tasks such as classification, forecasting, graph learning, and scientific modeling. Most systems are hybrid: classical networks prepare or process data, while parameterized quantum circuits encode features or serve as trainable model layers; common designs include data re-uploading, quantum convolutional circuits, and quantum attention. Work also explores how encoding, entanglement, circuit architecture, and measurement choices affect expressivity and trainability, with tests ranging from standard image and time-series benchmarks to real quantum hardware. A central tension is whether compact circuits can offer reliable benefits as qubit counts grow, given noise, costly training, and barren plateaus; convincing evidence requires comparisons with strong, parameter-matched classical models.

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135 papers11 papers in the last four weeks, down 15% on the four weeks before. 0.1% of all new papers.

Conformalized Quantum DeepONet Ensembles: Towards Scalable Operator Learning with Distribution-Free Guarantees
May 1, 2026Purav Matlia, Christian Moya, Guang LinUncertainty Quantification Hybrid Quantum-Classical ML
Department of Computer Science Purdue University · Department of Mathematics Purdue University · Department of Mathematics Department of Mechanical Engineering Purdue University
Efficient Mutation Testing of Quantum Machine Learning Models
Apr 30, 2026Emma Andrews, Prabhat MishraAutomated Software Testing Mutation Testing
University of Florida Gainesville, Florida, USA
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
Universal Quantum Transformer
Apr 29, 2026Sungyong Chung, Alireza TalebpourVariational Quantum Circuits Modular Arithmetic
Grainger College of Engineering, Department of Civil and Environmental Engineering, University of Illinois Urbana-Champaign
QERNEL: a Scalable Large Electron Model
Apr 28, 2026Khachatur Nazaryan, Liang FuNeural Quantum States Materials Science
Department of Physics, Massachusetts Institute of Technology, Cambridge, MA-02139,USA
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
QuanForge: A Mutation Testing Framework for Quantum Neural Networks
Apr 22, 2026Minqi Shao, Shangzhou Xia, Jianjun ZhaoAutomated Software Testing Mutation Testing
Kyushu University, Fukuoka, Japan
Option Pricing on Noisy Intermediate-Scale Quantum Computers: A Quantum Neural Network Approach
Apr 20, 2026Sebastian Zając, Rafał PrachtQuantitative Finance Quantum Neural Networks
finQbit Warsaw, Poland
Quantum inspired qubit qutrit neural networks for real time financial forecasting
Apr 20, 2026Kanishk Bakshi, Kathiravan SrinivasanQuantitative Finance Time Series Forecasting
School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India.
Path-Based Quantum Meta-Learning for Adaptive Optimization of Reconfigurable Intelligent Surfaces
Apr 20, 2026Noha Hassan, Xavier Fernando, Halim YanikomerogluAdaptive Model Routing Reconfigurable Intelligent Surfaces
Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada · Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada
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
Quantum Machine Learning for Colorectal Cancer Data: Anastomotic Leak Classification and Risk Factors
Apr 15, 2026Vojtěch Novák, Ivan Zelinka, Lenka Přibylová +3Clinical Outcome Prediction Clinical Risk Prediction
Department of Computer Science, Technical University of Ostrava, Czech Republic · IT4Innovations National Supercomputing Center, Ostrava, Czech Republic · Department of Applied Mathematics, Technical University of Ostrava, Czech Republic +3
Design Space Exploration of Hybrid Quantum Neural Networks for Chronic Kidney Disease
Apr 15, 2026Muhammad Kashif, Hanzalah Mohamed Siraj, Nouhaila Innan +2Quantum Neural Networks Hybrid Quantum-Classical ML
eBrain Lab, Division of Engineering, New York University Abu Dhabi, PO Box 129188, Abu Dhabi, UAE · Center for Quantum and Topological Systems, NYUAD Research Institute, New York University Abu Dhabi, UAE
Magnitude Is All You Need? Rethinking Phase in Quantum Encoding of Complex SAR Data
Apr 14, 2026Sakthi Prabhu Gunasekar, Prasanna Kumar RangarajanSAR ATR Quantum Data Encoding
Dept. of Computer Science & Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, India
QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits
Apr 13, 2026Navid Azimi, Aditya Prakash, Yao Wang +1Adversarial Robustness Hybrid Quantum-Classical ML
Department of Computer Science, Emory University, 201 Dowman Drive, Atlanta, 30322, GA, USA. · Department of Physics, Emory University, 201 Dowman Drive, Atlanta, 30322, GA, USA.
QNAS: A Neural Architecture Search Framework for Accurate and Efficient Quantum Neural Networks
Apr 8, 2026Kooshan Maleki, Alberto Marchisio, Muhammad ShafiqueQuantum Architecture Search Quantum Neural Networks
Department of Computer Engineering, Amirkabir University of Technology, Tehran, Iran · eBRAIN Lab, Division of Engineering, New York University Abu Dhabi (NYUAD), Abu Dhabi, UAE · Center for Quantum and Topological Systems (CQTS), NYUAD Research Institute, NYUAD, Abu Dhabi, UAE
HQF-Net: A Hybrid Quantum-Classical Multi-Scale Fusion Network for Remote Sensing Image Segmentation
Apr 8, 2026Md Aminur Hossain, Ayush V. Patel, Siddhant Gole +2Remote Sensing Image Segmentation Multi-Scale Feature Fusion
Space Applications Centre, ISRO, India · Indian Institute of Technology Bombay
Shot-based quantum encoding: a data-loading paradigm for quantum neural networks
Apr 7, 2026Basil Kyriacou, Viktoria Patapovich, Maniraman Periyasamy +1Quantum Data Encoding Quantum Machine Learning
Terra Quantum AG, 9000 St. Gallen, Switzerland
Eliminating Vendor Lock-In in Quantum Machine Learning via Framework-Agnostic Neural Networks
Apr 6, 2026Poornima Kumaresan, Shwetha Singaravelu, Lakshmi Rajendran +1Quantum Neural Networks Hybrid Quantum-Classical ML
Intrinsic Lab, Centre for Sensors, Instrumentation and Cyber-Physical System Engineering (SeNSE), Indian Institute of Technology Delhi, New Delhi 110016, India
LieTrunc-QNN: Lie Algebra Truncation and Quantum Expressivity Phase Transition from LiePrune to Provably Stable Quantum Neural Networks
Apr 3, 2026Haijian Shao, Dalong Zhao, Xing Deng +2Quantum Circuit Expressibility Quantum Neural Networks
School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212003, China · Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, USA
Q-DIVER: Integrated Quantum Transfer Learning and Differentiable Quantum Architecture Search with EEG Data
Mar 30, 2026Junghoon Justin Park, Yeonghyeon Park, Jiook ChaQuantum Machine Learning Hybrid Quantum-Classical ML
Seoul National University, Seoul, Korea
Transcending Classical Neural Network Boundaries: A Quantum-Classical Synergistic Paradigm for Seismic Data Processing
Mar 25, 2026Zhengyi Yuan, Xintong Dong, Xinyang Wang +2Quantum Neural Networks Hybrid Quantum-Classical ML
College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130026, China.
Model selection in hybrid quantum neural networks with applications to quantum transformer architectures
Mar 23, 2026Harsh Wadhwa, Rahul Bhowmick, Naipunnya Raj +3Quantum Circuit Expressibility Quantum Neural Networks
Quantum Lab, Fujitsu Research of India
Q-AGNN: Quantum-Enhanced Attentive Graph Neural Network for Intrusion Detection
Mar 23, 2026Devashish Chaudhary, Sutharshan Rajasegarar, Shiva Raj PokhrelNetwork Intrusion Detection Graph Attention Networks
School of Information Technology, Deakin University, Geelong, Victoria, 3220, Australia.
Layered Quantum Architecture Search for 3D Point Cloud Classification
Mar 20, 2026Natacha Kuete Meli, Jovita Lukasik, Vladislav Golyanik +1Quantum Architecture Search Quantum Neural Networks
University of Siegen · MPI for Informatics, SIC
How to find expressible and trainable parameterized quantum circuits?
Mar 15, 2026Peter Röseler, Dennis Willsch, Kristel MichielsenQuantum Circuit Expressibility Variational Quantum Circuits
Jülich Supercomputing Centre, Forschungszentrum Jülich, 52425 Jülich, Germany · Universität zu Köln, 50923 Köln, Germany · Faculty of Medical Engineering and Technomathematics, University of Applied Sciences Aachen, 52428 Jülich, Germany +1
Quantum entanglement provides a competitive advantage in adversarial games
Mar 11, 2026Peiyong Wang, Kieran Hymas, James QuachQuantum RL Quantum Machine Learning
Technology, CSIRO Clayton, Research Way, Clayton, VIC 3168, Australia.
Mitigating Frequency Learning Bias in Quantum Models via Multi-Stage Residual Learning
Mar 10, 2026Ammar DaskinQuantum Machine Learning Quantum Neural Networks
Department of Computer Engineering, Istanbul Medeniyet University, Istanbul, Turkiye, 34000
Characterization and upgrade of a quantum graph neural network for charged particle tracking
Mar 9, 2026Matteo Argenton, Laura Cappelli, Concezio BozziQuantum Neural Networks High-Energy Physics
Department of Physics and Earth Science, University of Ferrara, Via Saragat 1, Ferrara, 44122, Italy · INFN Ferrara, Via Saragat 1, Ferrara, 44122, Italy

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Conformalized Quantum DeepONet Ensembles: Towards Scalable Operator Learning with Distribution-Free Guarantees

Purav Matlia, Christian Moya, Guang Lin

Department of Computer Science Purdue University · Department of Mathematics Purdue University · Department of Mathematics Department of Mechanical Engineering Purdue University

Uncertainty Quantification Hybrid Quantum-Classical ML Deep Operator Networks Quantum Neural Networks

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Operator learning enables fast surrogate modelling of high-dimensional dynamical systems, but existing approaches face two fundamental limitations: the quadratic cost of dense neural layers and unreliable uncertainty quantification in safety-critical settings. We propose Conformalized Quantum DeepONet Ensembles, a framework that addresses both challenges simultaneously. Using Quantum Orthogonal Neural Networks (QOrthoNNs), we characterize the resource regime in which their established

\widetilde{\mathcal{O}}(n)

hidden-layer running-time scaling improves on the

\mathcal{O}(n^2)

cost of a classical dense layer. To quantify uncertainty, we combine ensemble predictions with split conformal calibration. For a new input-output function pair jointly exchangeable with the calibration pairs, we prove a finite-sample, distribution-free lower bound on the expected fraction of covered query locations. As a secondary proof-of-concept, we explore hybrid classical-quantum architectures and superposed execution to manage ensemble runtime and hardware requirements. Experiments on synthetic operator benchmarks and real-world power-system dynamics show accurate predictions under ideal simulation and empirical coverage near the target under both ideal conditions and selected compact-circuit simulations with depolarizing or device-calibrated composite noise. Together, these results connect resource-aware scalability analysis with finite-sample uncertainty guarantees for quantum operator learning.