Learning Non-Markovian Noise via Ensemble Optimal Control

Fuente: arXiv
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Main Authors: Luo, Da-Wei, Yu, Ting
Format: Preprint
Published: 2026
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author Luo, Da-Wei
Yu, Ting
author_facet Luo, Da-Wei
Yu, Ting
contents We study the estimation of parameters pertaining to non-Markovian quantum open systems, such as the dissipation rate and environmental memory time. A key challenge is identifying the optimal measurement time, which must allow sufficient time to acquire information about the environment, yet be short enough to avoid dissipation that erases the information. Using machine learning approaches, we develop an optimized control scheme trained over a representative ensemble to fix the optimal measurement time at a prescribed runtime. The protocol is robust to errors in the training process, enhances precision by exploiting non-Markovian memory effects, and achieves measurement uncertainties approaching the quantum limits set by the Cramér-Rao bound.
format Preprint
id arxiv_https___arxiv_org_abs_2604_16784
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Learning Non-Markovian Noise via Ensemble Optimal Control
Luo, Da-Wei
Yu, Ting
Quantum Physics
We study the estimation of parameters pertaining to non-Markovian quantum open systems, such as the dissipation rate and environmental memory time. A key challenge is identifying the optimal measurement time, which must allow sufficient time to acquire information about the environment, yet be short enough to avoid dissipation that erases the information. Using machine learning approaches, we develop an optimized control scheme trained over a representative ensemble to fix the optimal measurement time at a prescribed runtime. The protocol is robust to errors in the training process, enhances precision by exploiting non-Markovian memory effects, and achieves measurement uncertainties approaching the quantum limits set by the Cramér-Rao bound.
title Learning Non-Markovian Noise via Ensemble Optimal Control
topic Quantum Physics
url https://arxiv.org/abs/2604.16784