Quantum Machine Learning

Also known as QML

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Overview

Quantum machine learning studies how quantum states, measurements, and computation can support learning from classical or quantum data for tasks such as prediction, generation, sensing, and decision-making. Common approaches include variational quantum circuits and hybrid neural networks, quantum kernels and Gaussian processes, generative models, and learning-theoretic tools such as classical shadows and PAC bounds. Researchers test these methods on image and time-series tasks, molecular prediction, quantum-state and many-body dynamics learning, and reinforcement learning, while addressing data encoding, measurement cost, noise, trainability, and generalization. Evidence remains mixed: particular structured or quantum-data problems may benefit, but broad computational advantage over carefully tuned classical methods is not established and depends on fair, resource-matched evaluation.

Latest papers

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256 papers32 papers in the last four weeks, up 7% on the four weeks before. 0.2% of all new papers.

  1. Provable and scalable quantum Gaussian processes for quantum learning

    Apr 30, 2026Jonas Jäger, Paolo Braccia, Pablo Bermejo +3Quantum Machine LearningQuantum Kernel Methods

    Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA · Department of Computer Science and Institute of Applied Mathematics, University of British Columbia, Vancouver, V6T 1Z4 B.C., Canada · Stewart Blusson Quantum Matter Institute, Vancouver, V6T 1Z4 B.C., Canada +8

  2. Defending Quantum Classifiers against Adversarial Perturbations through Quantum Autoencoders

    Apr 30, 2026Emma Andrews, Sahan Sanjaya, Prabhat MishraAdversarial RobustnessQuantum Machine Learning

    University of Florida, Gainesville, Florida, USA

  3. Controlled Steering-Based State Preparation for Adversarial-Robust Quantum Machine Learning

    Apr 30, 2026Sahan Sanjaya, Hari Krishna Parvatham, Emma Andrews +1Adversarial RobustnessQuantum Machine Learning

    University of Florida, Gainesville, FL, USA

  4. Quantum Feature Selection with Higher-Order Binary Optimization on Trapped-Ion Hardware

    Apr 29, 2026Carlos Flores-Garrigós, Anton Simen, Qi Zhang +6Quantum Machine LearningFeature Selection

    Kipu Quantum GmbH, Greifswalderstrasse 212, 10405 Berlin, Germany · IonQ Inc., 4505 Campus Dr, College Park, MD 20740, USA · IDAL, Electronic Engineering Department, ETSE-UV, University of Valencia, Avgda. Universitat s/n, 46100 Burjassot, Valencia, Spain

  5. Parameterized Quantum Circuits as Feature Maps: Representation Quality and Readout Effects in Multispectral Land-Cover Classification

    Apr 29, 2026Ralntion Komini, Aikaterini Mandilara, Georgios Maragkopoulos +1Quantum Feature MapsLand Cover Classification

    Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Greece · Eulambia Advanced Technologies Ltd, Agiou Ioannou 24, Ag. Paraskevi, 15342, Greece

  6. Quantum Kernel Advantage over Classical Collapse in Medical Foundation Model Embeddings

    Apr 27, 2026Sebastian Cajas Ordóñez, Felipe Ocampo Osorio, Dax Enshan Koh +10Chest X-Ray ClassificationHealthcare

    1MIT Critical Data, Massachusetts Institute of Technology, Cambridge, MA, USA · 2Clinical Research Center, Artificial Intelligence Unit, Fundaci´on Valle del Lili, Cali, Valle del Cauca, Colombia · 3Quantum Innovation Centre (Q.InC), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore 138634, Republic of Singapore +15

  7. Do Quantum Transformers Help? A Systematic VQC Architecture Comparison on Tabular Benchmarks

    Apr 27, 2026Chi-Sheng Chen, En-Jui KuoQuantum Circuit ExpressibilityQuantum Machine Learning

    Beth Israel Deaconess Medical Center & Harvard Medical School Boston, MA, USA · Department of Electrophysics National Yang Ming Chiao Tung University Hsinchu, Taiwan

  8. Quantum AI for Cancer Diagnostic Biomarker Discovery

    Apr 18, 2026Mandeep Kaur Saggi, Amandeep Singh Bhatia, Humaira Gowher +1Quantum Machine Learning

    Department of Electrical and Computer Engineering, North Carolina State University, NC, USA · Department of Biochemistry, Purdue University, IN, USA · Department of Chemistry, North Carolina State University, Raleigh, NC 27695

  9. Discovering quantum phenomena with Interpretable Machine Learning

    Apr 17, 2026Paulin de Schoulepnikoff, Hendrik Poulsen Nautrup, Hans J. Briegel +1Interpretable MLQuantum Machine Learning

    University of Innsbruck, Department for Theoretical Physics, Technikerstr. 21a, A-6020 Innsbruck, Austria

  10. A Fully GPU-Accelerated Framework for High-Performance Configuration Interaction Selection with Neural Network Quantum States

    Apr 17, 2026Daran Sun, Bowen Kan, Haoquan Long +13GPU Kernel OptimizationGPU Acceleration

    Institute of Computing Technology, Chinese Academy of Sciences · University of Chinese Academy of Sciences · University of Science and Technology of China

  11. How Embeddings Shape Graph Neural Networks: Classical vs Quantum-Oriented Node Representations

    Apr 16, 2026Nouhaila Innan, Antonello Rosato, Alberto Marchisio +1Quantum Machine LearningQuantum-Inspired ML

    eBRAIN Lab, Division of Engineering, New York University Abu Dhabi (NYUAD), Abu Dhabi, UAE · Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, Rome, Italy · Center for Quantum and Topological Systems (CQTS), NYUAD Research Institute, NYUAD, Abu Dhabi, UAE

  12. Cloning is as Hard as Learning for Stabilizer States

    Apr 16, 2026Nikhil Bansal, Matthias C. Caro, Gaurav MahajanQuantum Machine Learning

    Department of Computer Science, University of Warwick, Coventry, UK · Department of Computer Science, Yale University, Connecticut, USA

  13. Parameter-efficient Quantum Multi-task Learning

    Apr 15, 2026Hevish Cowlessur, Chandra Thapa, Tansu Alpcan +1Multi-Task LearningQuantum Machine Learning

    Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, 3010, VIC, Australia. · CSIRO, Sydney, NSW, Australia.

  14. Magnitude Is All You Need? Rethinking Phase in Quantum Encoding of Complex SAR Data

    Apr 14, 2026Sakthi Prabhu Gunasekar, Prasanna Kumar RangarajanSAR ATRQuantum Data Encoding

    Dept. of Computer Science & Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, India

  15. Exponential quantum advantage in processing massive classical data

    Apr 8, 2026Haimeng Zhao, Alexander Zlokapa, Hartmut Neven +4Quantum Machine Learning

    California Institute of Technology, Pasadena, California 91125, USA · Google Quantum AI, Venice, California 90291, USA · Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA +1

  16. QNAS: A Neural Architecture Search Framework for Accurate and Efficient Quantum Neural Networks

    Apr 8, 2026Kooshan Maleki, Alberto Marchisio, Muhammad ShafiqueQuantum Architecture SearchQuantum 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

  17. Domain-Aware Hybrid Quantum Learning via Correlation-Guided Circuit Design for Crime Pattern Analytics

    Apr 8, 2026Niloy Das, Apurba Adhikary, Sheikh Salman Hassan +4Quantum Machine LearningHybrid Quantum-Classical ML

    Department of Information and Communication Engineering, Noakhali Science and Technology University, Bangladesh · Institute for Imaging, Data, and Communications (IDCOM), The University of Edinburgh, UK · School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China +2

  18. Shot-based quantum encoding: a data-loading paradigm for quantum neural networks

    Apr 7, 2026Basil Kyriacou, Viktoria Patapovich, Maniraman Periyasamy +1Quantum Data EncodingQuantum Machine Learning

    Terra Quantum AG, 9000 St. Gallen, Switzerland

  19. Learning and Generating Mixed States Prepared by Shallow Channel Circuits

    Apr 1, 2026Fangjun Hu, Christian Kokail, Milan Kornjača +5Quantum Generative ModelingQuantum State Preparation

    QuEra Computing Inc. · School of Engineering and Applied Sciences, Harvard University · JILA and Department of Physics, CU Boulder

  20. Learning from imperfect quantum data via unsupervised domain adaptation with classical shadows

    Mar 30, 2026Kosuke Ito, Akira Tanji, Hiroshi Yano +2Quantum Machine LearningUnsupervised Domain Adaptation

    Advanced Material Engineering Division, Toyota Motor Corporation, 1200 Mishuku, Susono, Shizuoka 410-1193, Japan · Quantum Computing Center, Keio University, Hiyoshi 3-14-1, Kohoku, Yokohama, Kanagawa 223-8522, Japan · Department of Applied Physics and Physico-Informatics, Keio University, Hiyoshi 3-14-1, Kohoku, Yokohama, Kanagawa 223-8522, Japan +2

  21. Q-DIVER: Integrated Quantum Transfer Learning and Differentiable Quantum Architecture Search with EEG Data

    Mar 30, 2026Junghoon Justin Park, Yeonghyeon Park, Jiook ChaQuantum Machine LearningHybrid Quantum-Classical ML

    Seoul National University, Seoul, Korea

  22. Q-Bridge: Code Translation for Quantum Machine Learning via LLMs

    Mar 29, 2026Runjia Zeng, Priyabrata Senapati, Ruixiang Tang +2Quantum Machine LearningLLM Code Generation

    Rochester Institute of Technology, USA · Kent State University, USA · Rutgers University, USA

  23. Enabling ab initio geometry optimization of strongly correlated systems with transferable deep quantum Monte Carlo

    Mar 26, 2026P. Bernát Szabó, Zeno Schätzle, Frank NoéVariational Monte CarloQuantum Chemistry

    FU Berlin, Department of Mathematics and Computer Science, Arnimallee 6, 14195 Berlin, Germany · Microsoft Research AI4Science, Karl-Liebknecht Str. 32, 10178 Berlin, Germany · FU Berlin, Department of Physics, Arnimallee 14, 14195 Berlin, Germany +1

  24. Spectral methods: crucial for machine learning, natural for quantum computers?

    Mar 25, 2026Vasilis Belis, Joseph Bowles, Rishabh Gupta +2Quantum Machine LearningSpectral Methods

    Xanadu Quantum Technologies Inc., Toronto, Ontario, M5G 2C8, Canada

  25. A PAC-Bayesian approach to generalization for quantum models

    Mar 24, 2026Pablo Rodriguez-Grasa, Matthias C. Caro, Jens Eisert +3Generalization BoundsPAC-Bayesian Generalization Bounds

    Department of Physical Chemistry, University of the Basque Country UPV/EHU, 48080 Bilbao, Spain · EHU Quantum Center, University of the Basque Country UPV/EHU, 48080 Bilbao, Spain · TECNALIA, Basque Research and Technology Alliance (BRTA), 48160 Derio, Spain +4

  26. Quantum Random Forest for the Regression Problem

    Mar 24, 2026Kamil Khadiev, Liliya SafinaQuantum Machine LearningRandom Forest

    Institute of Computational Mathematics and IT, Kazan Federal University, Kazan, Russia · Zavoisky Physical-Technical Institute, FRC Kazan Scientific Center of RAS, Kazan, Russia

  27. Latent Style-based Quantum Wasserstein GAN for Drug Design

    Mar 23, 2026Julien Baglio, Yacine Haddad, Richard PolifkaMolecular GenerationQuantum Generative Modeling

    Center for Quantum Computing and Quantum Coherence (QC2), Department of Physics, University of Basel, Klingelbergstrasse 82, CH-4056 Basel, Switzerland · QuantumBasel, Schorenweg 44b, CH-4144 Arlesheim, Switzerland · Laboratory for High Energy Physics (LHEP), Physics Institute, Faculty of Sciences, University of Bern, Sidlerstrasse 5, CH-3012 Bern, Switzerland +1

  28. Probabilistic modeling over permutations using quantum computers

    Mar 23, 2026Vasilis Belis, Giulio Crognaletti, Matteo Argenton +2Quantum Machine Learning

    Xanadu Quantum Technologies, Toronto, ON, M5G 2C8, Canada∗ · Department of Physics, University of Trieste, Strada Costiera 11, 34151 Trieste, Italy · European Organization for Nuclear Research (CERN), CH-1211 Geneva, Switzerland +2

  29. Modeling Quantum Federated Autoencoder for Anomaly Detection in IoT Networks

    Mar 23, 2026Devashish Chaudhary, Sutharshan Rajasegarar, Shiva Raj PokhrelQuantum Federated LearningAnomaly Detection

    School of Information Technology, Deakin University, Geelong, Australia

  30. The Average Relative Entropy and Transpilation Depth determines the noise robustness in Variational Quantum Classifiers

    Mar 22, 2026Aakash Ravindra Shinde, Arianne Meijer - van de Griend, Jukka K. NurminenQuantum Machine LearningVariational Quantum Algorithms

    Department of Computer Science, University of Helsinki, Helsinki, Finland