arXiv Machine Learning By Taiqi Zhou, Weiyuan Gong

Characterizing Arbitrary Lindbladian Dynamics with a Few Pauli Measurements

Read the original on arXiv Machine Learning →

arXiv:2607. 23044v1 Announce Type: cross Abstract: Quantum devices are open systems whose dynamics interleave coherent evolution with dissipation, and benchmarking, error mitigation, and error correction all rest on a faithful model of both.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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
Jun 19

Optimal Ansatz-free Hamiltonian Learning In Situ

arXiv:2606. 19486v1 Announce Type: cross Abstract: Characterizing the features of a Hamiltonian that governs a quantum system serves as a fundamental subroutine of quantum device calibration, signal sensing, and error correction.

By Taiqi Zhou, Weiyuan Gong
arXiv Machine Learning
6d ago

Generative Learning for Quantum Measurement Design

arXiv:2608. 11396v1 Announce Type: cross Abstract: Extracting quantum information from a quantum state is a fundamental task of quantum computation, often requiring the estimation of many non-commuting observables under a finite measurement budget.

By Jun Dai, Olivier Nahman-L\'{e}vesque, Guillaume Rabusseau, Hong-Ye Hu, Cunlu Zhou
Hugging Face Trending Papers
Aug 11

Generative Learning for Quantum Measurement Design

Extracting quantum information from a quantum state is a fundamental task of quantum computation, often requiring the estimation of many non-commuting observables under a finite measurement budget. For both near-term and early fault-tolerant settings, the measurement protocol must balance statistical efficiency against implementation resources such as circuit depth, connectivity, and entangling-gate count.