Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session M36: Superconducting Quantum Information Processing
8:00 AM–10:24 AM,
Wednesday, March 16, 2022
Room: McCormick Place W-194A
Sponsoring
Unit:
DQI
Chair: Andrew Lingenfelter, University of Chicago
Abstract: M36.00008 : Non-linear Amplifiers for Quantum State Readout*
9:24 AM–9:36 AM
Presenter:
Leon Y Bello
(Princeton University)
Authors:
Leon Y Bello
(Princeton University)
Ryan Kaufman
(University of Pittsburgh)
Saeed A Khan
(Princeton University)
Michael J Hatridge
(University of Pittsburgh)
Hakan E Tureci
(Princeton University)
Recurrent neural networks have been employed for dispersive readout, allowing improvements in state classification, although with a costly training overhead. We instead consider the application of reservoir computing to qubit readout tasks, which forgoes hardware optimization in favor of a trained linear output layer [1, 2], efficiently trained by solving a simple convex optimization problem. We apply this framework to dispersive qubit readout using a SNAIL-based parametric amplifier. Particularly, we analyze the operation of this amplifier as a reservoir across complex dynamical regimes that are not conventionally considered for qubit readout. We find that the reservoir can be successfully applied to multi-state classification of qubits across these regimes, and outperforms standard linear filters while requiring only a fraction of the training data to achieve the same fidelity.
*This research was developed with funding from the Defense Advanced Research Projects Agency contract HR00112190072. The views, opinions, and findings expressed are those of the authors and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government.This work was supported by ARO.
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