Digital Twin Modeling for NV Magnetometry
Name
rich-jackrich-meng-eecs-2025-thesis.pdf
Description
Thesis PDF
Size
4.17 MB
Format
Adobe PDF
Checksum (MD5)
d1df22edff766044e28c77fe21cdec4e
Author(s)
Rich, John P.
Advisor(s)
Englund, Dirk R.
Date Issued
May 2025
Publisher
Massachusetts Institute of Technology
Abstract
This thesis presents the development and application of a digital twin modeling framework for nitrogen-vacancy (NV) center-based magnetometry, advancing the field of quantum sensing. A surrogate model serves as a computational representation of the physical NV magnetometer system, enabling comprehensive exploration of parameter spaces to optimize device design. Leveraging machine learning techniques, this study optimizes control mechanisms, including the design of learned analog filters, to enhance system performance. This research investigates the fundamental limits of NV magnetometer performance, identifying strategies to minimize power requirements while maintaining high sensitivity. A dynamic framework is implemented to update the surrogate model’s parameters in real-time based on experimental measurements, ensuring accurate fidelity to the physical system. Additionally, the optimized control strategies are simulated within the digital twin environment, demonstrating their potential for advanced quantum sensing applications such as magnetocardiography (MCG) for heartbeat detection.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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In Copyright - Educational Use Permitted
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