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High-dimensional multi-fidelity Bayesian optimization for quantum control
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DOI：10.1088/2632-2153/ad0100.png)
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Abstract
En 中文
We present the first multi-fidelity Bayesian optimization (BO) approach for solving inverse problems in the quantum control of prototypical quantum systems. Our approach automatically constructs time-dependent control fields that enable transitions between initial and desired final quantum states. Most importantly, our BO approach gives impressive performance in constructing time-dependent control fields, even for cases that are difficult to converge with existing gradient-based approaches. We provide detailed descriptions of our machine learning methods as well as performance metrics for a variety of machine learning algorithms. Taken together, our results demonstrate that BO is a promising approach to efficiently and autonomously design control fields in general quantum dynamical systems.
Keywords:
Bayesian optimization
quantum control
multi-fidelity
Journal
M
IF:
4.6
Papers:
1.1K
Citations:
3.4K
