arXiv AI By Nathan X. Kodama, L. Andrew Wray, Sam Cochran, Chad Rigetti, Shravan Veerapaneni, Michael J. Keiser

Scalable quantum simulation of continuous-time generative models via tensor networks

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arXiv Machine Learning
Jun 9

Quantum latent distributions in deep generative models

arXiv:2508. 19857v3 Announce Type: replace Abstract: Many successful families of generative models leverage a low-dimensional latent distribution that is mapped to a data distribution.

By Omar Bacarreza, Thorin Farnsworth, Alexander Makarovskiy, Hugo Wallner, Tessa Hicks, Santiago Sempere-Llagostera, John Price, Robert J. A. Francis-Jones, William R. Clements
arXiv Machine Learning
Jun 16

Stochastic Schr\"odinger Diffusion Models for Pure-State Ensemble Generation

arXiv:2605. 03573v3 Announce Type: replace-cross Abstract: Quantum machine learning increasingly relies on pure-state representations, motivating generative models that sample directly in quantum representation space rather than perturbing classical inputs and re-encoding.

By Jian Xu, Wei Chen, Shigui Li, Chao Li, Jingyuan Zheng, Delu Zeng, John Paisley, Qibin Zhao
Hugging Face Trending Papers
Jul 8

An Hybrid Quantum-Classical Diffusion Model for Image Generation

Quantum diffusion models provide a physics-consistent route to generative learning by formulating noising and denoising directly on quantum states. However, applying such models to classical high-dimensional data is constrained by the qubit cost of state encoding and the computational burden of simulating large density operators.