Bulletin of the American Physical Society
54th Annual Meeting of the APS Division of Atomic, Molecular and Optical Physics
Volume 68, Number 7
Monday–Friday, June 5–9, 2023; Spokane, Washington
Session M10: Quantum Sensors
2:00 PM–3:12 PM,
Wednesday, June 7, 2023
Room: 207
Chair: John Bollinger, NIST
Abstract: M10.00006 : Learning response functions: a data-driven framework for quantum sensing.*
3:00 PM–3:12 PM
Presenter:
Cinthia Huerta Alderete
(Los Alamos National Laboratory)
Authors:
Cinthia Huerta Alderete
(Los Alamos National Laboratory)
Max Hunter Gordon
(Los Alamos National Laboratory)
Frédéric Sauvage
(Los Alamos National Laboratory)
Akira Sone
(Aliro Technologies)
Andrew T Sornborger
(Los Alamos National Laboratory)
Patrick J Coles
(Los Alamos National Laboratory)
Marco Cerezo
(Los Alamos National Laboratory)
The success of this task hinges on the ability to correlate changes in the parameter to changes in the measurement outcomes. For simple cases, such as an idealized magnetometry experiment, the functional form of the system response is well-known. However, the same cannot be said for realistic scenarios as the explicit functional form may not be accessible and would require full device characterization. In this work, we present a novel data-driven inference approach to recover the true response of the system in an efficient and scalable manner. We provide rigorous theoretical guarantees for the performance of our framework, which we verify with numerical simulations and experiments on IBM quantum computers.
*This work was supported by NSEC Quantum Sensing at Los Alamos National Laboratory (LANL), the U.S. Department of Energy (DOE), Office of Science, Office of Advanced Scientific Computing Research, under the Quantum Computing Application Teams (QCAT) program, the internal R&D from Aliro Technologies, Inc, the LANL ASC Beyond Moore's Law project, the LDRD program of LANL under project number 20210116DR and, the Quantum Science Center (QSC), a National Quantum Information Science Research Center of the U.S. Department of Energy (DOE). This research used quantum computing resources provided by the LANL Institutional Computing Program, which is supported by the U.S. DOE National Nuclear Security Administration under Contract No. 89233218CNA000001.
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