Bulletin of the American Physical Society
APS March Meeting 2021
Volume 66, Number 1
Monday–Friday, March 15–19, 2021; Virtual; Time Zone: Central Daylight Time, USA
Session B21: Machine Learning for Quantum Matter II
11:30 AM–2:18 PM,
Monday, March 15, 2021
Sponsoring
Units:
DCOMP GDS DMP
Chair: Mohamed Hibat-Allah, University of Waterloo
Abstract: B21.00006 : Continuous monitoring and feedback control of qubit dynamics using differentiable programming
12:54 PM–1:06 PM
Live
Presenter:
Frank Schäfer
(University of Basel)
Authors:
Frank Schäfer
(University of Basel)
Pavel Sekatski
(University of Basel)
Martin Koppenhoefer
(University of Basel)
Niels Loerch
(University of Basel)
Christoph Bruder
(University of Basel)
Michal Kloc
(University of Basel)
To solve this inverse problem, we simulate the dynamics of the quantum system as part of a fully differentiable program [1]. Specifically, we employ a neural network that selects control parameters to be applied in the next time-step based on the current state of the qubit or the observed sequence of measurement results. These control parameters modify the equation of motion of the system. The parameters of the neural network are optimized in a series of epochs based on gradient information obtained efficiently by (adjoint) sensitivity methods. We verify the robustness of our method in different scenarios.
[1] F. Schäfer et al., Mach. Learn.: Sci. Technol.,*1* 035009 (2020)
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