Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session N70: Quantum Sensing Fundamentals
11:30 AM–2:30 PM,
Wednesday, March 8, 2023
Room: Room 409
Sponsoring
Unit:
DQI
Chair: Lev Krayzman, Princeton University
Abstract: N70.00004 : Inference-based quantum sensing*
12:06 PM–12:18 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)
In a standard Quantum Sensing (QS) task one aims at estimating an unknown parameter encoded into an n-qubit probe state, via measurements of the system. The success of this task hinges on the ability to correlate changes in the parameter to changes in the system response (i.e., changes in the measurement outcomes). For simple cases the form of the system response is known, but the same cannot be said for realistic scenarios, as no general closed-form expression exists. In this work we present an inference-based scheme for QS. We show that, for a general class of unitary families of encoding the response function can be fully characterized by only performing measurements at 2n+1 parameters. This allows us to infer the value of an unknown parameter given the measured response, as well as to determine the sensitivity of the scheme, which characterizes its overall performance. We show that the inference error can be well controlled if one measures the system response using a number of shots that scales poly-logarithmically with the system size. Furthermore, the framework presented can be broadly applied as it remains valid for arbitrary probe states and measurement schemes, and, even holds in the presence of quantum noise. We also discuss how to extend our results beyond unitary families. Finally, to showcase our method we implement it for a QS task on real quantum hardware, and in numerical simulations.
*CHA acknowledge support by NSEC Quantum Sensing at Los Alamos National Laboratory (LANL). This work was also supported by 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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