| Autor: |
D. L. Craig, N. Ares, E. M. Gauger |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Physical Review Research, Vol 6, Iss 4, p 043175 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2643-1564 |
| DOI: |
10.1103/PhysRevResearch.6.043175 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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