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arXiv Machine Learning By Yulong Dong, Jiaqi Leng

Quantum score matching with applications to learning thermal states

Read the original on arXiv Machine Learning →

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%.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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