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.

Variational Quantum Conditional Boltzmann Machines for Time-Series Forecasting: Architectures, Symmetric Hyperparameter Evaluation, and a Nonlinear Benchmark
Jul 27, 2026Gerhard Hellstern, Danyal Maheshwari, Martin Zaefferer +2Time Series Forecasting Forecasting Benchmarks
Centre of Finance DHBW Stuttgart Stuttgart, Germany · Zentrum für Digitale Innovationen DHBW Ravensburg Ravensburg, Germany · DATEV eG Nürnberg, Germany
Quantum Spectral Model: Data Reuploading with Input-Conditioned Frequency Support
Jul 24, 2026Peiyong Wang, Udaya Parampalli, Casey R. MyersQuantum Machine Learning Spectral Methods
CSIRO Technology · Research Way, Clayton VIC 3168, Australia · School of Computing and Information Systems +6
Cautious optimism for deep parameterized quantum circuits
Jul 23, 2026Marie Kempkes, Elies Gil-Fuster, Carlos Bravo-Prieto +5Double Descent Neural Network Generalization
Leiden University, Niels Bohrweg 1, 2333 CA Leiden, Netherlands · Volkswagen Group Innovation, Berliner Ring 2, 38440 Wolfsburg, Germany · Dahlem Center for Complex Quantum Systems, Freie Universität Berlin, 14195 Berlin, Germany +4
Explainable quantum-compressed machine learning for complex fluid flows
Jul 23, 2026Xiao Xue, Maida Wang, Mingyang Gao +2Neural Surrogate Modeling Interpretable ML
Centre for Computational Science, University College London, London, UK · Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities, Boltzmannstraße 1, 85748 Garching, Germany · Centre for Advanced Research Computing, University College London, London, UK
Classical Hardware Acceleration of Quantum Autoencoders for Real-Time Anomaly Detection in Collider Experiments
Jul 22, 2026Ivan Ge, Sagar Addepalli, Abhilasha Dave +1Online Anomaly Detection Quantum Machine Learning
Department of Physics, Stanford University, 450 Jane Stanford Way, Stanford, 94305, CA, USA. · SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, 94025, CA, USA.
A Drift Stable Quantum Federated Learning for Intelligent Services
Jul 22, 2026Shanika Iroshi Nanayakkara, Shiva Raj PokhrelQuantum Machine Learning Deep Unfolding
School of IT, Deakin University, VIC 3125, Burwood, Australia
Hybrid LLM-Guided Search for Quantum Reservoir Architecture Design
Jul 21, 2026Krishna Bhatia, Gautami Sanjay NaikLarge Language Model-Guided Optimization Quantum Machine Learning
QuantumAI Lab, Fractal Analytics India · Indian Institute of Information Technology Dharwad India
Quantum Reservoir Computing: Recent Advances and Future Directions
Jul 20, 2026Shehbaz Tariq, Muhammad Talha, Arshid Ali +2Quantum Machine Learning Reservoir Computing
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg City, Luxembourg · School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, United Kingdom
Interpreting Quantum Learning Models via Stochastic Processes
Jul 19, 2026Johannes Fankhauser, Lukas J. Fiderer, Hans J. BriegelQuantum Machine Learning
University of Innsbruck, Institute for Theoretical Physics, Technikerstrasse 21a, A-6020 Innsbruck, Austria
Rethinking Quantum Continual Learning with Quantum Fisher Information
Jul 17, 2026Yu-Chao Hsu, Yu-Cheng Lin, Tai-Yue Li +2Quantum Machine Learning Variational Quantum Circuits
School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea · Department of Electrophysics, National Yang Ming Chiao Tung University, Hsinchu 300092, Taiwan. · National Center for High-Performance Computing , National Institutes of Applied Research (NIAR), Hsinchu 300092, Taiwan
From hyperplanes to hyperellipsoids: a pedagogical bridge from linear classifiers to single-qubit mixed-state classifiers
Jul 16, 2026Kaitlin GiliQuantum Machine Learning Binary Classification
QodeX Quantum
Quantum Topological Data Encoding
Jul 15, 2026Adam Wesołowski, Dimitrios Thanos, Daniel Leykam +1Quantum Machine Learning Quantum Data Encoding
Royal Holloway University of London, Department of Computer Science, UK · Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, The Netherlands · Science, Mathematics and Technology Cluster, Singapore University of Technology and Design, Singapore +1
PQFA: Parallel Quantum Feature Augmentation of Fused Representations for Multimodal Classification
Jul 15, 2026Mingzhu Wang, Yun ShangQuantum Feature Maps Quantum Machine Learning
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China · School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China · State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China
MQAdapter: Multi-Modal Quantum Adapter for Coarse-to-Fine VLM Fine-tuning
Jul 14, 2026Yumiao Zhao, Bo Jiang, Min Lu +2VLM Adaptation Quantum Machine Learning
Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University, Hefei, China · Origin Quantum Computing Company Limited, Hefei, Anhui 230026, China
$\mathtt{Q^2SAR}$ : overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning
Jul 13, 2026Mariano Caruso, Daniel Ruiz, Alejandro Giraldo +1Quantum Machine Learning Molecular Property Prediction
UGR, Granada, Spain · UNIR, La Rioja, Spain · FIDESOL, Granada, Spain +4
When cheap gradients fail: the measurement cost of attacking quantum classifiers
Jul 13, 2026Bacui Li, Chandra Thapa, Tansu Alpcan +1Quantum Machine Learning Variational Quantum Circuits
Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, Victoria 3010, Australia · CSIRO Data61, Marsfield, NSW 2122, Australia · School of Computing and Information Systems, University of Melbourne, Parkville, Victoria 3010, Australia
Overcoming Fourier Locking in Quantum Data Re-uploading Classifiers via Spectral Homotopy
Jul 13, 2026Spencer TopelQuantum Machine Learning Variational Quantum Circuits
Moth, Brooklyn, New York
When Classical Baselines Are Tuned as Carefully as the Quantum Model, Does Quantum Reservoir Computing Still Win?
Jul 10, 2026Tushar PandeyTime Series Forecasting Forecasting Benchmarks
Texas A&M University
Quantum-Enhanced Synthetic Data Generation Using Quantum Circuit Born Machines for Imbalanced Tabular Learning
Jul 10, 2026Tanapol Nuatho, Narisorn Sangnakara, Prapong Prechaprapranwong +1Class-Imbalanced Learning Quantum Machine Learning
Department of Computer Engineering King Mongkut’s University of Technology Thonburi Bangkok, Thailand
Equivariant Quantum Clustering with Differential Privacy: Parameter-Efficient Privacy-Preserving Analysis Across Heterogeneous Sensitive Datasets
Jul 9, 2026B. M. Taslimul Haq, Md Arifur Rahman, Tawfiq Al Islam Foysal +2Clustering Quantum Machine Learning
Department of Information Systems, Central Michigan University, Mount Pleasant, MI, USA. · Department of Computer and Information Sciences, Trine University, Angola, IN, USA. · Department of Computer Science and Engineering, American International University–Bangladesh (AIUB), Dhaka, Bangladesh. +2
Quantum simulation of real-world nonlinear dynamics via Koopman method
Jul 8, 2026Baoyang Zhang, Dong An, Zhaoyuan Meng +4Dynamical Systems Koopman Operator Learning
State Key Laboratory for Turbulence and Complex Systems, School of Mechanics and Engineering Science, Peking University, Beijing 100871, China · Beijing International Center for Mathematical Research, Peking University, Beijing 100871, China · Institute of Mechanics, State Key Laboratory of Nonlinear Mechanics, Chinese Academy of Sciences, Beijing 100190, China +2
An Hybrid Quantum-Classical Diffusion Model for Image Generation
Jul 8, 2026Qipeng Qian, Keli Deng, Yuntao QianLatent Diffusion Models Quantum Generative Modeling
Program of Applied Mathematics, Department of Mathematics, University of Arizona, Tucson, USA · College of Computer Science and Technology, Zhejiang University, Hangzhou, China
Provable learning separation for predicting time-evolution of quantum many-body systems
Jul 7, 2026Rahul Bandyopadhyay, Riccardo Molteni, Jens Eisert +2Quantum Machine Learning Quantum Many-Body Physics
⟨aQaL⟩, Applied Quantum Algorithms, Leiden University, The Netherlands · Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands · Dahlem Center for Complex Quantum Systems, Freie Universität Berlin, 14195 Berlin, Germany +2
Entanglement as a Structural Complexity Axis: A PAC-Bayesian View of Generalization in Quantum Policies and Value Functions
Jul 7, 2026Jian Xu, Delu Zeng, John Paisley +1PAC-Bayesian Generalization Bounds Quantum Machine Learning
1RIKEN iTHEMS · 2RIKEN AIP · 3South China University of Technology +1
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
Canonical quantization of neurons
Jul 6, 2026Alexander He, Nana Liu, Mark M. WildeQuantum Machine Learning Hybrid Quantum-Classical ML
Department of Physics, Cornell University, Ithaca, New York 14850, USA · Institute of Natural Sciences, School of Mathematical Sciences, Ministry of Education Key Laboratory in Scientific and Engineering Computing, and Global College, Shanghai Jiao Tong University, Shanghai 200240, China · School of Electrical and Computer Engineering, Cornell University, Ithaca, New York 14850, USA
Ravines in quantum cost landscapes: opportunities for improved VQA predictions
Jul 1, 2026Felix J. Beckmann, João F. BravoQuantum Machine Learning Variational Quantum Algorithms
Fraunhofer Institute IAO, Stuttgart, Germany. · Karlsruhe Institute of Technology, Karlsruhe, Germany.
Quantum vs. Classical Machine Learning: A Unified Empirical Comparison
Jul 1, 2026Chuanming Yu, Jiaming Liu, Zihao Ge +4Quantum Machine Learning Quantum RL
Hebei Normal University, Shijiazhuang 050024, China · University of Luxembourg, Kirchberg L-1359, Luxembourg · Kyushu University, Fukuoka 819-0395, Japan
When AI meets quantum information: A comprehensive review
Jul 1, 2026Min Chen, Yu Gan, Xin Jin +15Quantum Machine Learning
Department of Computer Science, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA · Joint Center for Quantum Information and Computer Science, NIST and University of Maryland, College Park, MD 20742, USA · Joint Quantum Institute, NIST and University of Maryland, College Park, MD 20742, USA +11
Generative Modeling of Quantum Distribution with Functional Flow Matching
Jul 1, 2026Jaehoon Hahm, Tak Hur, Joonseok Lee +1Flow Matching Quantum Generative Modeling
Department of Statistics and Data Science, Yonsei University, Seoul, Korea · Graduate School of Data Science, Seoul National University, Seoul, Korea · Google Research, Mountain View, California, United States +1

Jul 27, 2026J/K move · Enter open · S save

Variational Quantum Conditional Boltzmann Machines for Time-Series Forecasting: Architectures, Symmetric Hyperparameter Evaluation, and a Nonlinear Benchmark

Gerhard Hellstern, Danyal Maheshwari, Martin Zaefferer, Martin Braun+1

Centre of Finance DHBW Stuttgart Stuttgart, Germany · Zentrum für Digitale Innovationen DHBW Ravensburg Ravensburg, Germany · DATEV eG Nürnberg, Germany

Time Series Forecasting Forecasting Benchmarks Quantum Machine Learning Hybrid Quantum-Classical ML Restricted Boltzmann Machines

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In this study, we developed and evaluated four conditional energy-based forecasting architectures: a classical Gaussian-Bernoulli CRBM, a hybrid quantum-classical QCRBM, a full-register QQRBM, and a lag-feature QFeatureQRBM with complete derivations of their conditional distributions, Contrastive-Divergence gradients, and hybrid training, bridging the energy-based formulation and the implementation-level quantum computation. Unlike prior comparisons, our evaluation enforces symmetric hyperparameter optimisation: classical and quantum-specific hyperparameters receive an equally thorough grid search across thirteen structured experiments. We test on two data classes, a Gaussian-process dataset (GP) generated with real financial data and the input-driven NARMA-10 nonlinear benchmark. Across both regimes we find no systematic evidence of a quantum advantage at the available sample size: no quantum architecture improves on the best classical baseline. The fully quantum QQRBM and QFeatureQRBM are significantly worse, whereas the hybrid QCRBM is statistically indistinguishable from the strongest classical CRBM on both datasets. A power analysis bounds this null result: at n = 12 only medium-to-large effects are detectable, so small advantages cannot be excluded. An iso-parameter (matched-budget) comparison reaches the same conclusion: the classical CRBM is lowest at three of the four budgets and no CRBM-vs-QCRBM difference is significant at any budget.