Differentiable master equation solver for quantum device characterization
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 a...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2024-11-01
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| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/PhysRevResearch.6.043175 |
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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 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. |
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| ISSN: | 2643-1564 |