Experimental graybox quantum system identification and control

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.

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• Dynamically Error-Corrected Gates for Universal Quantum Computation
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Cited by in corpus (5)

• Artificial Intelligence for Quantum Computing
• Programmable quantum circuits in a large-scale photonic waveguide array
• Real-time adaptive estimation of decoherence timescales for a single qubit
• Differentiable master equation solver for quantum device characterisation
• Model Order Reduction for Open Quantum Systems Based on Measurement-adapted Time-coarse Graining