Simulation-assisted learning of open quantum systems

TL;DR

This paper introduces a simulation-assisted learning method for inferring parameters in open quantum systems, combining direct quantum master equation simulation with measurement data to improve model calibration, especially with large measurement intervals.

Contribution

It proposes a novel learning approach that integrates a guaranteed-accuracy simulation technique for quantum master equations to infer system parameters from data.

Findings

The method accurately estimates parameters in Markovian open quantum systems.

Simulation preserves complete positivity with guaranteed accuracy.

Validated with error estimates and numerical experiments.

Abstract

Models for open quantum systems, which play important roles in electron transport problems and quantum computing, must take into account the interaction of the quantum system with the surrounding environment. Although such models can be derived in some special cases, in most practical situations, the exact models are unknown and have to be calibrated. This paper presents a learning method to infer parameters in Markovian open quantum systems from measurement data. One important ingredient in the method is a direct simulation technique of the quantum master equation, which is designed to preserve the completely-positive property with guaranteed accuracy. The method is particularly helpful in the situation where the time intervals between measurements are large. The approach is validated with error estimates and numerical experiments.