High-Dimensional Multi-Fidelity Bayesian Optimization for Quantum Control (2023)

by Marjuka F. Lazin, Christian R. Shelton, Simon N. Sandhofer, and Bryan M. Wong

Abstract: We present the first multi-fidelity Bayesian optimization 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 Bayesian optimization 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 Bayesian optimization is a promising approach to efficiently and autonomously design control fields in general quantum dynamical systems.

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Marjuka F. Lazin, Christian R. Shelton, Simon N. Sandhofer, and Bryan M. Wong (2023). "High-Dimensional Multi-Fidelity Bayesian Optimization for Quantum Control." Machine Learning: Science and Technology, 4(4).

Bibtex citation

@article{Lazetal23,
   author = "Marjuka F. Lazin and Christian R. Shelton and Simon N. Sandhofer and Bryan M. Wong",
   title = "High-Dimensional Multi-Fidelity {B}ayesian Optimization for Quantum Control",
   journal = "Machine Learning: Science and Technology",
   journalabbr = "MLST",
   year = 2023,
   volume = 4,
   number = 4,
   doi = "10.1088/2632-2153/ad0100",
}