Differentiable master equation solver for quantum device characterisation

Differentiable models of physical systems provide a powerful platform for gradient-based algorithms, with particular impact on parameter estimation and optimal control. Quantum systems present a particular challenge for such characterisation and control, owing to their inherently stochastic nature a...

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Bibliographic Details
Published in:arXiv.org 2024-08
Main Authors: Craig, David L, Ares, Natalia, Gauger, Erik M
Format: Article
Language:English
Subjects:
Algorithms
Bayesian analysis
Charge transport
Machine learning
Mathematical models
Optimal control
Parameter estimation
Parameter sensitivity
Quantum dots
Solvers
Statistical inference
Online Access:Get full text
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Summary:Differentiable models of physical systems provide a powerful platform for gradient-based algorithms, with particular impact on parameter estimation and optimal control. Quantum systems present a particular challenge for such characterisation and control, owing to their inherently stochastic nature and sensitivity to environmental parameters. To address this challenge, we present a versatile differentiable quantum master equation solver, and incorporate this solver into a framework for device characterisation. Our approach utilises gradient-based optimisation and Bayesian inference to provide estimates and uncertainties in quantum device parameters. To showcase our approach, we consider steady state charge transport through electrostatically defined quantum dots. Using simulated data, we demonstrate efficient estimation of parameters for a single quantum dot, and model selection as well as the capability of our solver to compute time evolution for a double quantum dot system. Our differentiable solver stands to widen the impact of physics-aware machine learning algorithms on quantum devices for characterisation and control.
ISSN:2331-8422
DOI:10.48550/arxiv.2403.04678