arXiv Machine Learning By Jian Xu, Wei Chen, Shigui Li, Chao Li, Jingyuan Zheng, Delu Zeng, John Paisley, Qibin Zhao

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

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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.

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arXiv Machine Learning
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Local-Time Riemannian Score Matching on the Quantum Pure-State Manifold

arXiv:2605. 03573v4 Announce Type: replace-cross Abstract: Score-based diffusion can be defined intrinsically on the manifold of quantum pure states, $\mathbb{CP}^{d-1}$ with the Fubini--Study metric, but no closed-form transition density is available, so the score must be supervised by a local-time teacher taken from the Euclidean limit of the diffusion in normal coordinates.

By Jian Xu, Wei Chen, Shigui Li, Chao Li, Delu Zeng, John Paisley, Qibin Zhao
arXiv Machine Learning
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Quantum score matching with applications to learning thermal states

The paper introduces a quantum score‑matching framework that extends classical score matching to quantum states, addressing challenges posed by noncommuting density operators. It demonstrates that this method can learn thermal (Gibbs) states without extra state preparation, achieving optimal sample complexity in high‑temperature regimes for local Hamiltonians. Numerical tests and experiments on IBM quantum hardware confirm the approach’s effectiveness and NISQ‑friendly performance, reducing Hamiltonian‑parameter error from 64% to about 10%.

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Is data-efficient learning feasible with quantum models?

arXiv:2508. 19437v2 Announce Type: replace-cross Abstract: The importance of analyzing nontrivial datasets when testing quantum machine learning (QML) models is becoming increasingly prominent in literature, yet a cohesive framework for understanding dataset characteristics remains elusive.

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