arXiv Machine Learning
Jun 30

Learning the structure of open quantum systems

arXiv:2606. 30358v1 Announce Type: cross Abstract: We design an algorithm for learning the coefficients of an $n$-qubit constant-local Lindbladian to $\varepsilon$ error with $O(g d^2 \log(n) / \varepsilon^2)$ total evolution time, where $g$ is the single-site energy and $d$ is the (approximate) degree of the interaction graph.

By Laura Lewis, Ewin Tang, John Wright
arXiv Machine Learning
Jul 24

Cautious optimism for deep parameterized quantum circuits

arXiv:2607. 21409v1 Announce Type: cross Abstract: A central challenge in quantum machine learning is understanding the scaling behavior of parameterized quantum circuits (PQCs).

By Marie Kempkes, Elies Gil-Fuster, Carlos Bravo-Prieto, Aroosa Ijaz, Alissa Wilms, Jens Eisert, Evert van Nieuwenburg, Vedran Dunjko
arXiv Machine Learning
Sep 24

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%.

By Yulong Dong, Jiaqi Leng