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.

Download Information

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",
}

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