Efficient learning of quantum interactions from thermal metastable states
Abstract
Learning quantum interactions from finite-temperature many-body systems is a central task in emerging quantum platforms. Recently, the problem of learning from lattice quantum Gibbs states has found rigorous, efficient protocols. Nevertheless, exact Gibbs states, as the input premise, are in fact computationally intractable to prepare and may not faithfully represent generic finite-temperature quantum systems. In contrast, a system coupled to a heat bath can be stuck at an approximate stationary state (metastable state) long before it truly equilibrates. Here, we formulate a physically and algorithmically consistent alternative: learning from such metastable states of detailed-balanced master equations (Lindbladians) arising from system-bath interactions. We distill the algorithmic mechanism and structural condition underlying Gibbs-state learning and extend it in full to metastable states, attaining nearly optimal sample and computational complexity (in the system size and the precision). More broadly, we sharpen notions of metastability and develop a unified framework for finite-temperature learning.
Related
- Near-Optimal Learning of Quantum Many-Body Systems from Gibbs and Thermal Metastable States via the Stationarity Test
- Diagonal, Switching, and Locally Optimal Unraveling for efficient quantum trajectories in metastable open quantum systems
- Coherence-assisted quantum state preparation and thermal control through repeated interactions
- Thermal Metastable Strings in One-Scale Models and Gravitational Waves
- Interactions and dynamics of metastable triplet-state helium dimer with Ne and Ar
BibTeX
@misc{wang2026efficient,
title = {{Efficient learning of quantum interactions from thermal metastable states}},
author = {Bingrun Wang and Qi Ye and Chi-Fang Chen},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.01538},
eprint = {2610.01538},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.01538},
}
RIS
TY - UNPB
TI - Efficient learning of quantum interactions from thermal metastable states
AU - Bingrun Wang
AU - Qi Ye
AU - Chi-Fang Chen
PY - 2026
DA - 2026/10/01
PB - arXiv
DO - 10.48550/arxiv.2610.01538
UR - https://arxiv.org/abs/2610.01538
AB - Learning quantum interactions from finite-temperature many-body systems is a central task in emerging quantum platforms. Recently, the problem of learning from lattice quantum Gibbs states has found rigorous, efficient protocols. Nevertheless, exact Gibbs states, as the input premise, are in fact computationally intractable to prepare and may not faithfully represent generic finite-temperature quantum systems. In contrast, a system coupled to a heat bath can be stuck at an approximate stationary state (metastable state) long before it truly equilibrates. Here, we formulate a physically and algorithmically consistent alternative: learning from such metastable states of detailed-balanced master equations (Lindbladians) arising from system-bath interactions. We distill the algorithmic mechanism and structural condition underlying Gibbs-state learning and extend it in full to metastable states, attaining nearly optimal sample and computational complexity (in the system size and the precision). More broadly, we sharpen notions of metastability and develop a unified framework for finite-temperature learning.
ER -