Differentiable master equation solver for quantum device characterization
AI Breakdown
Get a structured breakdown of this paper — what it's about, the core idea, and key takeaways for the field.
Abstract
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 characterization 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 facilitating direct computation of steady-state solutions, and incorporate this solver into a framework for device characterization capable of dealing with additional nondifferentiable parameters. Our approach utilizes gradient-based optimization 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 characterization and control. Published by the American Physical Society 2024