Quantum-Inspired ML

ML: Machine Learning

Quantum-inspired machine learning uses ideas from quantum mechanics to build learning models, often running entirely on classical computers. Approaches include density-matrix and complex-valued representations, tensor networks, quantum-walk and variational-circuit-inspired layers, Kolmogorov-Arnold networks with fast weights, and QUBO optimization. These methods are tested in settings such as image and signal classification, graph learning, forecasting, anomaly detection and scientific inference, with attention to accuracy as well as model size, runtime and communication costs. Results show task-specific benefits, but no general advantage: fair comparisons with strong classical baselines, simulation overhead, scalability and generalization remain central challenges.

Latest papers

47 papers2 papers in the last four weeks, down 33% on the four weeks before. 0.0% of all new papers.

Quantum-Inspired Convolutional Networks for High-Resolution Pseudo-Label Generation from Multimodal Earth Observation Data
Oct 29, 2026Abhishek Singh, Giovanni Cuozzo, Alexander Jacob +1Pseudo-Labeling Weakly Supervised Semantic Segmentation
Institute for Earth Observation, Eurac Research, Bolzano, Italy
PFedQFT: Personalized Federated Quantum-Inspired Fine-Tuning for Medical Large Language Models
Oct 19, 2026Qihan Huang, Lingxia Zhang, Guye Zhang +2Federated Fine-Tuning Federated Learning
Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan, China · Key Laboratory of Quantum Physics and Photonic Quantum Information, Ministry of Education, University of Electronic Science and Technology of China, Chengdu, Sichuan, China · Sichuan Provincial Key Laboratory for Human Disease Gene Study and the Center for Medical Genetics, Department of Laboratory Medicine, Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China +1
QuanVI: Score-based Variational Inference via Quantum Maximally Mixed States
Sep 30, 2026Yuchen Cong, Zerui Tao, Chao Li +2Bayesian Inference Quantum-Inspired ML
Juntendo University · RIKEN-AIP
QiT: Quantum-Inspired Transformer for Visual Recognition Task
Sep 15, 2026Badri N. Patro, Vijay AgneeswaranVision Transformer Quantum-Inspired ML
Microsoft
Quantum-Inspired Trainable and Parameter-Efficient Tensor Networks for Image Inpainting
Sep 15, 2026Shiwen An, Konstantinos SlavakisTensor Networks Quantum-Inspired ML
Institute of Science Tokyo, Department of Information and Communications Engineering, Yokohama, Japan
A Quantum-Inspired Dequantization Method for Diagonally Weighted Matrix Functions: Application to Learning with Optimized Random Features
Sep 9, 2026Natsuto Isogai, Mio Murao, Hayata YamasakiQuantum-Inspired ML Random Feature Methods
Department of Physics, Graduate School of Science, The University of Tokyo, Tokyo, Japan · Department of Computer Science, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan
Seasonal and Quantum-inspired Models for Neutron Monitor Time Series Forecasting
Aug 26, 2026Krishna Bhatia, Shalini Devendrababu, Srinjoy GangulyTime Series Forecasting Quantum-Inspired ML
Quantum AI Lab, Fractal AI Research, India
Quantum-Inspired Modeling of Driving Behavior
Aug 26, 2026Mohammad Elayan, Omid Armantalab, Wissam KontarTraffic Flow Theory Intelligent Transportation Systems
Civil & Environmental Engineering, University of Nebraska–Lincoln, Lincoln, NE, USA
QuantumPhaseNet: A Gauge-Covariant Geometric and Quantum-Spectral Theory of Semantic Concept Hierarchies with Prototype Validation of a Classical Quantum-Inspired Model
Aug 16, 2026Kiyotaka Kasubuchi, Kazuo FukiyaSemantic Representation Learning Quantum-Inspired ML
Hybrid Quantum-inspired Kolmogorov-Arnold Networks for Privacy-Aware Federated Biosignal Learning
Aug 14, 2026Chun-Hua Lin, Samuel Yen-Chi Chen, Yu-Chao Hsu +7Electrocardiogram Classification Kolmogorov-Arnold Networks
Department of Physics and Center for Theoretical Physics, National Taiwan University, Taipei, Taiwan · National Center for High-Performance Computing, National Institutes of Applied Research, Hsinchu, Taiwan · Brookhaven National Laboratory, Upton, NY, USA +5
Quantum-Structured World Models (QSWMs) for Predictive Latent Dynamics
Aug 5, 2026Hailong Jiang, Emran Hossain, Feng Yu +3Latent Dynamics Modeling Quantum-Inspired ML
Youngstown State University, Youngstown, OH, USA · Kent State University · George Washington University
LegoQ: Density-Matrix Representation Learning with Spectral-Spatial State Transitions for Hyperspectral Classification
Jul 31, 2026Weijia Cao, Xiaofei Yang, Fu Wang +2Representation Learning Quantum-Inspired ML
Complementary Matrix-Gated QKAN Fast-Weight Programmers for Quantum Dynamics Forecasting
Jul 30, 2026Kuo-Chung Peng, Samuel Yen-Chi Chen, Jiun-Cheng Jiang +14Quantum-Inspired ML Kolmogorov-Arnold Networks
Department of Physics and Center for Theoretical Physics, National Taiwan University, Taipei, Taiwan · Wells Fargo, New York, NY, USA · NVIDIA AI Technology Center, NVIDIA Corp., Taipei, Taiwan +10
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
Formulation-Level Auto-Tuning for QUBO-Based Machine Learning: A Case Study Across Multiple Quantum-Inspired Annealers
Jul 21, 2026Naoya Mizuki, Takahiro Katagiri, Daichi Mukunoki +1Black-Box Optimization Quantum-Inspired ML
Graduate School of Informatics, Nagoya University · Information Technology Center, Nagoya University
Image classification via a quantum-inspired strategy involving a mixture of experts
Jul 8, 2026Kumari Jyoti, Rohith Babu, Apoorva D. PatelHybrid Quantum-Classical ML Quantum-Inspired ML
Centre for High Energy Physics, Indian Institute of Science, Bangalore 560012
Quantum-Inspired Vision: Leveraging Wave-Particle Duality for Low-Illumination Enhancement
Jul 2, 2026Yiquan GaoExplainable Artificial Intelligence Low-Light Image Enhancement
Heriot-Watt University
RiverONE: Generating Knowledge-Intensive VLM by Simulated Quantum Machines
Jun 29, 2026Xindian Ma, Xinyu Long, Yefei Zhang +11Vision-Language Models Quantum-Inspired ML
Thewake RiverYtz Lab
Parameter-Efficient Quantum-Inspired Fast Weight Programmers for Traffic-Matrix Forecasting
Jun 26, 2026Kuo-Chung Peng, Jiun-Cheng Jiang, Chun-Hua Lin +3Quantum-Inspired ML Spatiotemporal Forecasting
Department of Physics and Center for Theoretical Physics, National Taiwan University, Taipei, Taiwan · National Center for High-Performance Computing, National Institutes of Applied Research, Hsinchu, Taiwan · Wells Fargo, New York, NY, USA
Quantum-Inspired Contextual Learning for Sparse-Ring Fraud Detection in Dynamic Transaction Graphs
Jun 22, 2026Behnam Tonekaboni, Hiroshi YamauchiFinancial Fraud Detection Quantum-Inspired ML
1Infleqtion Australia, Melbourne · 2SoftBank Corp., Japan, Tokyo
A Matched Spectral Benchmark of Quantum Inspired Feature Maps
May 23, 2026Toheeb Ogunade, Taofeek Kassim, Etinosa OsaroQuantum Feature Maps Quantum Machine Learning
Department of Computer Science, University of Lagos, Lagos, Nigeria · Department of Physics, University of Lagos, Lagos, Nigeria · Department of Chemical Engineering, University of Notre Dame, Indiana, USA
QuantFPFlow: Quantum Amplitude Estimation for Fokker--Planck Policy Optimisation in Continuous Reinforcement Learning
May 14, 2026Abraham Itzhak WeinbergReinforcement Learning Quantum-Inspired ML
AI-WEINBERG, AI Experts, Tel Aviv, Israel
Winning Lottery Tickets in Neural Networks via a Quantum-Inspired Classical Algorithm
May 13, 2026Natsuto Isogai, Hayata Yamasaki, Sho Sonoda +1Lottery Ticket Hypothesis Quantum-Inspired ML
The University of Tokyo · RIKEN AIP
CTQWformer: A CTQW-based Transformer for Graph Classification
May 10, 2026Zhan Li, Wuqing Yu, Yusen Wu +1Graph Representation Learning Quantum-Inspired ML
school of Artificial Intelligence, Beijing Normal University, Beijing, China
A Quantum Inspired Variational Kernel and Explainable AI Framework for Cross Region Solar and Wind Energy Forecasting
May 9, 2026Pavan Manjunath, Thomas PruferPower Systems Time Series Forecasting
Independent Research Scholar, Germany
Exploring the non-convexity in machine learning using quantum-inspired optimization
May 8, 2026Kandula Eswara Sai Kumar, Parth Dhananjay Danve, Abhishek Chopra +1Evolutionary Optimization Quantum-Inspired ML
1*BosonQ Psi (BQP), New York, USA.
Gated QKAN-FWP: Scalable Quantum-inspired Sequence Learning
May 7, 2026Kuo-Chung Peng, Samuel Yen-Chi Chen, Jiun-Cheng Jiang +16Time Series Forecasting Quantum-Inspired ML
Department of Physics and Center for Theoretical Physics, National Taiwan University, Taipei, Taiwan · National Center for High-Performance Computing, National Institutes of Applied Research, Hsinchu, Taiwan · Wells Fargo, New York, NY, USA +11
Generative Quantum-inspired Kolmogorov-Arnold Eigensolver
May 6, 2026Yu-Cheng Lin, Yu-Chao Hsu, I-Shan Tsai +9Quantum Generative Modeling Quantum Chemistry
Department of Electrophysics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan. · National Center for High-Performance Computing, National Institutes of Applied Research, Hsinchu, Taiwan · Cross College Elite Program, National Cheng Kung University, Tainan, Taiwan +7
QYOLO: Lightweight Object Detection via Quantum Inspired Shared Channel Mixing
Apr 29, 2026Garvit Kumar Mittal, Sahil Tomar, Sandeep KumarEfficient Neural Network Inference Quantum-Inspired ML
Central Research Laboratory Bharat Electronics Limited Ghaziabad, India
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.

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Quantum-Inspired Convolutional Networks for High-Resolution Pseudo-Label Generation from Multimodal Earth Observation Data

Abhishek Singh, Giovanni Cuozzo, Alexander Jacob, Claudia Notarnicola

Institute for Earth Observation, Eurac Research, Bolzano, Italy

Pseudo-Labeling Weakly Supervised Semantic Segmentation Multimodal Semantic Segmentation Multimodal SSL Quantum-Inspired ML

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Accurate land-cover mapping from multimodal Earth observation (EO) data remains a challenge in regions where high-resolution annotations are scarce or costly to obtain. This work introduces a quantum convolutional encoder-decoder network for pseudo-label generation using Sentinel-1 SAR and Sentinel-2 multispectral imagery, guided only by coarse MODIS-derived land-cover maps. The proposed framework uses quantum-inspired convolutional blocks to enhance feature extraction and cross-sensor fusion, enabling the model to infer a fine-scale semantic structure from heterogeneous inputs. Experiments were conducted on the DFC2020 dataset, which provides a quadruple of Sentinel-1 SAR, Sentinel-2 multispectral, low-resolution MODIS labels and high-resolution reference land-cover maps. The proposed training framework first learns a robust representation of multimodal inputs through the reconstruction branch before any supervision from low-resolution labels is applied. This self-supervised stage enables the model to capture a fine-scale spectral-spatial structure, which is then used to generate high-resolution pseudo-labels. These pseudo-labels are subsequently evaluated through classification metrics on the test data, demonstrating that the proposed approach produces more spatially coherent and class-consistent compared to classical baselines. The results highlight the potential of quantum-inspired deep learning to improve label generation in low-annotation remote sensing scenarios, offering a scalable path towards high-quality land-cover products for EO applications.