Experimental graybox quantum system identification and control

TL;DR

This paper presents an experimental 'graybox' approach that combines physics principles with machine learning to identify and control quantum systems more effectively than traditional methods, even in complex, noisy environments.

Contribution

The paper introduces a novel graybox methodology for quantum system identification and control that outperforms model fitting and provides physical insights, applicable to time-dependent and open systems.

Findings

Superior performance over model fitting in quantum control

Ability to generate unitaries and Hamiltonians not accessible from standard models

Effective in noisy and complex quantum environments

Abstract

Understanding and controlling engineered quantum systems is key to developing practical quantum technology. However, given the current technological limitations, such as fabrication imperfections and environmental noise, this is not always possible. To address these issues, a great deal of theoretical and numerical methods for quantum system identification and control have been developed. These methods range from traditional curve fittings, which are limited by the accuracy of the model that describes the system, to machine learning methods, which provide efficient control solutions but no control beyond the output of the model, nor insights into the underlying physical process. Here we experimentally demonstrate a "graybox" approach to construct a physical model of a quantum system and use it to design optimal control. We report superior performance over model fitting, while generating unitaries and Hamiltonians, which are quantities not available from the structure of standard supervised machine learning models. Our approach combines physics principles with high-accuracy machine learning and is effective with any problem where the required controlled quantities cannot be directly measured in experiments. This method naturally extends to time-dependent and open quantum systems, with applications in quantum noise spectroscopy and cancellation.