Quantum Machine Learning

Also known as QML

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

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

Towards unsupervised representation learning for quantum data: quantum models with inference and generation
Aug 31, 2026Robin Lorenz, Eric Brunner, Marcello BenedettiLatent Variable Models Quantum Generative Modeling
Quantinuum, London, United Kingdom
Quantum-Grassmann-Plucker Token Mixing for Deep Learning-Based Post-Disaster Damage Assessment
Aug 31, 2026Kooroush Farahkhah, Umut Lagap, Taha Rezaei +1Disaster Damage Assessment Quantum Machine Learning
Independent Researcher, Tabriz, Iran. · Centre for Urban Resilience and Analytics (CURA), Georgia Institute of Technology (Georgia Tech), US. · Department of Civil and Environmental Engineering, University of Exeter, Exeter, UK. +1
End-to-End Quantum Semantic Communication with Variational Quantum Neural Networks
Aug 30, 2026Melek Krichen, Nikhitha Nunavath, Riccardo Bassoli +2Quantum Machine Learning
Polytechnique Montréal Montréal, Canada · TU Dresden Dresden, Germany
Representation Learning with Quantum Signal Processing
Aug 28, 2026Junqi Wang, Junyu LiuRepresentation Learning Quantum Machine Learning
Department of Computer Science, University of Pittsburgh, Pittsburgh, PA 15260, USA
Quantum Federated Learning Based on Bures--Uhlmann Geometry for Heterogeneous Noisy Clients
Aug 28, 2026Haruki Emori, Masaki Uchihara, Yuuki TokunagaQuantum Federated Learning Quantum Machine Learning
Graduate School of Information Science and Technology, Hokkaido University, Kita 14, Nishi 9, Kita-ku, Sapporo, Hokkaido 060-0814, Japan · RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS), RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan · Department of Diabetes, Endocrinology, and Metabolism, National Center for Global Health and Medicine, Japan Institute for Health Security, 1-21-1 Toyama, Shinjuku-ku, Tokyo 162-8655, Japan
Comparing Classical and Quantum Machine Learning for Regression in High Energy Physics Collision Data
Aug 28, 2026Tariq Mahmood, Zain ul Abidin, Itzel Luviano Soto +1Quantum Machine Learning Quantum Neural Networks
Centre for High Energy Physics, University of the Punjab, Lahore, Pakistan. · Facultad de Ingenier´ıa Civil, Universidad Michoacana de San Nicol´as de Hidalgo, Morelia, Michoac´an, M´exico · Facultad de Ingenier´ıa El´ectrica, Universidad Michoacana de San Nicol´as de Hidalgo, Morelia, Michoac´an, M´exico +1
Quantum SEDONet: Spectrally-Embedded Quantum Deep Operator Networks for Partial Differential Equations
Aug 27, 2026Muhammad Abid, Arth Sojitra, Bipin Tiwari +1PDE Operator Learning Deep Operator Networks
Department of Mechanical and Aerospace Engineering University of Tennessee, Knoxville Knoxville, TN 37996, USA
When Similarity Is Interaction-Driven: Quantum Kernels for Regime-Sensitive Learning
Aug 25, 2026Hanqiu Peng, Jianlong Lu, Ying ChenQuantum Feature Maps Feature Interaction Modeling
Centre for Quantitative Finance, Department of Mathematics Risk Management Institute, National University of Singapore, Singapore
A Theory of Finite-Noise Optima and Generalization in Quantum Machine Learning
Aug 25, 2026Ziyu Zhang, Zikang Jia, Xiaosong Li +1Quantum Machine Learning Statistical Learning Theory
Department of Chemistry, University of Washington, Seattle, Washington 98195, USA · Department of Mathematics, University of Michigan, Ann Arbor, Michigan 48109, USA · Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan 48109, USA
QML for Quantum Sensing under Measurement-Induced Information Loss
Aug 25, 2026Sounak Bhowmik, Himanshu ThapliyalQuantum Machine Learning
Department of Electrical and Computer Engineering Southern Methodist University, Dallas, TX, USA
Empirical Characterization of Learning Geometry in Hybrid Quantum Forecasting Models
Aug 19, 2026Sandra Leticia Juárez-Osorio, Jorge I. Hernandez-Martinez, Jesus Ivan Ruiz-Martinez +2Quantum Machine Learning Hybrid Quantum-Classical ML
Department of Computer Science, CINVESTAV Guadalajara, Mexico · Cinvestav Unidad Tamaulipas, Mexico
Quantum Gaussian processes for prediction of channel observations
Aug 19, 2026Jonas Jäger, Yaroslav Khmelnitskiy, Paolo Braccia +4Quantum Machine Learning Quantum System Identification
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 · Institute of Theoretical Physics, Jagiellonian University, Kraków, Poland. +6
Bernstein-Vazirani Networks: Quantum Machine Learning by Interference
Aug 19, 2026Natacha Kuete Meli, Tolga Birdal, Prayag Tiwari +2Quantum Machine Learning Quantum Neural Networks
Department of Vision and Graphics, University of Siegen · Department of Computing, Imperial College London · School of Information Technology, Halmstad University +1
Quantum Tensor Network Learning with DMRG
Aug 19, 2026Gustav J L Jäger, Martin B Plenio, Hans-Martin RieserTensor Networks Quantum Machine Learning
Deutsches Zentrum für Luft- und Raumfahrt e.V. – Institut für KI-Sicherheit Wilhelm-Runge-Straße 10, 89081 Ulm, Germany · Universität Ulm – Institut für Theoretische Physik and IQST, Albert-Einstein-Allee 11, 89081 Ulm, Germany
Quantum-Logic Tsetlin Machines: Interpretable Quantum Machine Learning with Commuting Projector Clauses
Aug 19, 2026Krishna BhatiaQuantum Machine Learning Interpretable ML
QuantumAI Lab, Fractal Analytics · QuantumAI Lab, Fractal Analytics, Mumbai, India
Dynamic Entanglement-Weighted Pruning for Quantum Federated Unlearning in Supply-Chain Risk Prediction
Aug 17, 2026Aditya Kumar, Sumit ChongderQuantum Federated Learning Federated Unlearning
IDRP - Quantum Information and Computation, Department of Physics & Computer Science and Engineering, Indian Institute of Technology Jodhpur, Jodhpur, Rajasthan 342030, India
Benchmarking Quantum Machine Learning for Power-System Attack Detection: Evaluation Choices Decide the Outcome Before the Models Do
Aug 16, 2026Md Rezwanul IslamQuantum Machine Learning Adversarial Robustness
Dhaka, Bangladesh
Quantum Models with Multi-Stage Training for Compositional Concept Generalization
Aug 16, 2026Mina Abbaszadeh, Matilda Karabina Moore, Mehrnoosh Sadrzadeh +1Compositional Generalization Compositional Representation Learning
Department of Computer Science University College London London, United Kingdom · Institute for Logic, Language and Computation University of Amsterdam Amsterdam, Netherlands
PAS-QFL: Personalized Ansatz Selection for Quantum Federated Learning under Client Data Heterogeneity
Aug 15, 2026Jindi Wu, Qun LiQuantum Federated Learning Federated Learning
School of computing DePaul University Chicago, USA · Computer science department William & Mary Williamsburg, USA
Classical Limits of Spectral Filtering in Quantum Generative Models
Aug 14, 2026Marco RothQuantum Circuit Born Machines Quantum Machine Learning
Fraunhofer Institute for Industrial Engineering IAO, Nobelstr. 12, D-70569 Stuttgart, Germany
Exponential quantum advantage for learning signals with a single qubit
Aug 13, 2026Ishaan Kannan, Sridhar Prabhu, Saeed A. Khan +7Quantum Machine Learning
Harvard Quantum Initiative, Harvard University, Cambridge, MA 02138, USA · School of Applied and Engineering Physics, Cornell University, Ithaca, NY 14853, USA · Department of Physics, Cornell University, Ithaca, NY 14853, USA +2
How Quantum Is the Advantage? A Fair, Calibration- and Noise-Aware Benchmark and Attribution Audit of Quantum Machine Learning for Network Intrusion Detection
Aug 13, 2026Syeda Anshrah Gillani, Mirza Samad Ahmed Baig, Shahid Munir Shah +2Quantum Machine Learning Hybrid Quantum-Classical ML
Hamdard University, Karachi, Pakistan · SZABIST University, Pakistan · Fandaqah, Al Khobar, Saudi Arabia
A Quantum/Classical Example Oracle Separation for Making Things Up
Aug 12, 2026Kenny ChenQuantum Machine Learning PAC Learning
The University of Sydney
A Quantum Roadmap for Softmax Attention: Exact Born-Rule Analogs for Softmax Attention on the Probability Simplex
Aug 11, 2026Eric A. F. Reinhardt, Adam J. HauserSoftmax Attention Quantum Machine Learning
Department of Physics and Astronomy University of Alabama, Tuscaloosa, AL 35487, USA
Quantum Incremental Learning with Mixed State Prototypes
Aug 11, 2026Yu Wu, Qianli Zhou, Xinyang Deng +3Class-Incremental Learning Prototype-Based Classification
School of Electronics and Information, Northwestern Polytechnical University, Xi’an, 710072, China · School of Computer Science and Technology, Northwestern Polytechnical University, Xi’an, 710072, China · School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371 +5
An Exploratory Evaluation of LLM-Assisted Rewriting of Moderate-Complexity Financial Sentences for DisCoCat-Based Sentiment Analysis
Aug 7, 2026Brian Llinas, Nikos ChrisochoidesFinancial Sentiment Analysis Quantum NLP
Department of Computer Science Old Dominion University Norfolk, VA, USA
How Much Reconstruction Does Quantum Machine Learning Need? Late Fusion of Independently Trained Quantum Subcircuits
Aug 6, 2026Prabhjot Singh, Adel N. Toosi, Rajkumar BuyyaLate Fusion Quantum Machine Learning
Quantum Cloud Computing and Distributed Systems (qCLOUDS) Lab, School of Computing and Information Systems, The University of Melbourne, Melbourne, VIC, 3010, Australia
Representational separation between unitary and channel quantum generative models via shared classical randomness at shallow depth
Aug 5, 2026Arunava Majumder, Marius Krumm, Hendrik Poulsen Nautrup +1Quantum Generative Modeling Quantum Circuit Expressibility
University of Innsbruck, Department of Theoretical Physics, Technikerstraße 21a, A-6020 Innsbruck, Austria.
Quantum Speedups for Stochastic Optimization with Heavy-Tailed Noise
Jul 28, 2026Bin Luo, Chengchang Liu, Jonathan Allcock +2Nonconvex Stochastic Optimization Heavy-Tailed Noise
The Chinese University of Hong Kong · Westlake University · Tencent Quantum Laboratory
Multivariate Time Series Forecasting with Adaptive Non-Local Observables
Jul 27, 2026Yu-Ting Lee, Huan-Hsin Tseng, Samuel Yen-Chi ChenMultivariate Time Series Forecasting Quantum Machine Learning
Graduate Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan · Brookhaven National Laboratory, AI & ML Department, Upton, NY, USA · Wells Fargo, New York, NY, USA

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Towards unsupervised representation learning for quantum data: quantum models with inference and generation

Robin Lorenz, Eric Brunner, Marcello Benedetti

Quantinuum, London, United Kingdom

Latent Variable Models Quantum Generative Modeling Quantum Machine Learning Unsupervised Representation Learning

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With quantum sensors, simulators and networks emerging, a future of quantum technology may produce quantum states as data---that is, coherently rather than as classical measurement records---thus motivating the study of suitable quantum generalisations of modern machine learning, including the automated, unsupervised extraction of useful representations. Two ingredients are central to the latter: inference, mapping observations to latent representations, and generation, mapping latent states back to synthetic data. Both are related to each other and to joint distributions for training models by the chain-rule of classical probability theory. The fact that quantum states however lack such universal, standard factorisation property thus poses a challenge. Here we develop a conceptual and mathematical framework for unsupervised representation learning from quantum data. Models are joint quantum states over visible and latent systems; state-over-time maps provide a notion of factorisation into a marginal state and inference (generation) channel; models with inference (generation) are ambiguous states---states for which such factorisation obtains---subject to a further consistency condition on extended inference maps as data extension. These stipulations are restrictive: we show that non-trivial models must feature non-linear such maps to the extended space. For three representative state-over-time maps, we completely characterise the ambiguous states, uncovering a hierarchy tied to the positive-partial-transpose (PPT) criterion from entanglement theory. Notably, the Leifer-Spekkens construction supports inference and generation exactly for model classes of PPT states, thus allowing genuinely quantum visible-latent correlations. We also formulate quantum counterparts of exact and approximate inference training, explore weaker notions of data extension and sketch a future research programme.