Machine Learning for Quantum-Enhanced Sensors

PI: Paola Cappellaro, Department of Nuclear Science and Engineering, MIT

Novel devices based on quantum mechanics are fostering a commercial revolution, yet their development is hampered by fundamental aspects of non-intuitive quantum effects, and the exponential growth with system size of computational resources that hinders  modeling.  This inherent complexity of quantum systems, is proving to be a promising area for machine learning that has very recently been shown to provide efficient representations of a few large quantum systems, and to greatly improve quantum control. Here we focus on the use of MIT’s Nitrogen Vacancy (NV) centers in diamond as a framework for quantum enhanced sensing.  We will improve qubit performance, by utilizing Skoltech’s NV control package and enhancing its function with machine learning. Optimal control applied to quantum enhanced sensing is still in its infancy and techniques from machine learning promise to increase sensor performance.  By working on specific examples and confronting real, experimental scenarios, we hope to plant the seeds for a future in-depth study.

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