Quantum Maximum Entropy Inference and Hamiltonian Learning
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
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.
arXiv:2607. 21409v1 Announce Type: cross Abstract: A central challenge in quantum machine learning is understanding the scaling behavior of parameterized quantum circuits (PQCs).
arXiv:2609.39164v1 Announce Type: new Abstract: Score-based variational inference (VI) provides an alternative to Kullback--Leibler (KL)-based VI by minimizing the Fisher divergence between the varia...
arXiv:2607. 01080v1 Announce Type: new Abstract: We investigate Gaussian process (GP) bandit optimization with quantum kernels, assuming the mean reward function lies in the reproducing kernel Hilbert space (RKHS) induced by the quantum kernel.
arXiv:2411. 03163v4 Announce Type: replace-cross Abstract: In this work, we initiate the study of Hamiltonian learning for positive temperature bosonic Gaussian states, the quantum generalization of the widely studied problem of learning Gaussian graphical models.
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%.