Quantum Circuit Born Machines

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8 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Quantum-JEPA: Quantum Born Priors for Latent-Action JEPA World Models
Oct 20, 2026Zhiyuan Liu, Sicheng Liu, Haiyu WuQuantum Circuit Born Machines Action-Conditioned World Models
Qinous Technology Co., Ltd., Beijing, China · Tsinghua University, Beijing, China · University of Notre Dame, Notre Dame, IN, USA
Hierarchical Fourier Approximation for Variational Quantum Distribution Learning
Sep 5, 2026Taha Hoseinpour Asli, Sajjad Hashemian, Ebrahim Ardeshir-LarijaniQuantum Circuit Born Machines Quantum Machine Learning
Sharif University of Technology · University of Tehran · Iran University of Science and Technology
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
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
Trainability of IQP Quantum Circuit Born Machines Under Gaussian Initialization
Jun 8, 2026Gennaro De Luca, Vinayak Sharma, Aviral ShrivastavaQuantum Generative Modeling Quantum Machine Learning
SCAI, Arizona State University
IQP Born Machines under Data-dependent and Agnostic Initialization Strategies
Mar 15, 2026Sacha Lerch, Joseph Bowles, Ricard Puig +3Quantum Circuit Born Machines Quantum Machine Learning
Institute of Physics, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland · Centre for Quantum Science and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland · Xanadu, Toronto, ON, M5G 2C8, Canada +3
A Hybrid Quantum Circuit Born Machine Framework for Financial Volatility Forecasting: Quantum-Assisted Training and Classical Inference
Mar 10, 2026Yixiong ChenQuantitative Finance Quantum Machine Learning
Quantum Scrambling Born Machine
Feb 19, 2026Marcin PłodzieńQuantum Circuit Born Machines Quantum Generative Modeling
Qilimanjaro Quantum Tech, Carrer de Veneçuela 74, 08019 Barcelona, Spain

Oct 20, 2026J/K move · Enter open · S save

Quantum-JEPA: Quantum Born Priors for Latent-Action JEPA World Models

Zhiyuan Liu, Sicheng Liu, Haiyu Wu

Qinous Technology Co., Ltd., Beijing, China · Tsinghua University, Beijing, China · University of Notre Dame, Notre Dame, IN, USA

Quantum Circuit Born Machines Action-Conditioned World Models Quantum Generative Modeling Probabilistic Trajectory Prediction Latent Action Learning

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A single observation can admit several plausible futures, making uncertainty an essential part of world modelling. However, quantum generative models remain underexplored for this task. We introduce Quantum-JEPA, a latent-action JEPA framework that learns distributions of future representations using a conditional quantum Born prior. Circuit measurements generate binary latent actions, which a shared predictor maps to alternative future representations. We train the quantum prior through the quality and diversity of these predictions using a sampled energy objective. Our model learns distinct continuations on constructed multimodal tasks and captures latent-action correlations that support coordinated multi-agent futures. On D3IL, a benchmark of diverse human demonstrations, our trained models generate multiple obstacle-crossing routes from a common starting point without observed motion. On the controlled tasks, our classical and quantum prior implementations improve prediction validity and distributional fidelity over the evaluated variational baselines. Our quantum priors combine competitive predictive quality with a parameter-efficient sampling structure: they avoid an exponentially sized categorical output layer while supporting correlated latent actions. We further validate finite-shot parameter-shift training, including training with readout noise. Unlike most prior work, which focuses on simulation, we also verify the feasibility of inference with trained priors on a real QPU. These results establish a trainable quantum component for JEPA future prediction and a basis for exploring quantum-enhanced world models at larger scales.