Bulletin of the American Physical Society
55th Annual Meeting of the APS Division of Atomic, Molecular and Optical Physics
Monday–Friday, June 3–7, 2024; Fort Worth, Texas
Session K00: Poster Session II (4pm-6pm CDT)
4:00 PM,
Wednesday, June 5, 2024
Room: Hall BC
Abstract: K00.00087 : Enhanced Measurement of Neutral Atom Qubits with Machine Learning*
Presenter:
Linipun Phuttitarn
(University of Wisconsin - Madison)
Authors:
Linipun Phuttitarn
(University of Wisconsin - Madison)
Brooke Becker
(University of Wisconsin-Madison)
Ravikumar Chinnarasu
(University of Wisconsin-Madison)
Trent Graham
(University of Wisconsin - Madison)
Mark Saffman
(University of Wisconsin - Madison, Infleqtion, Inc.)
In this poster, we demonstrate qubit state measurements assisted by a supervised convolutional neural network (CNN) in a neutral atom quantum processor. We present two CNN architectures for analyzing neutral atom qubit readout data: a compact 5-layer single-qubit CNN architecture and a 6-layer multi-qubit CNN architecture. We benchmark both architectures against a conventional Gaussian threshold analysis method. We demonstrate up to 56% reduction of measurement infidelity using the CNN compared to a conventional analysis method. This work presents a proof of concept for a CNN network to be implemented as a real-time readout processing method on a neutral atom quantum computer, enabling faster readout time and improved fidelity.
*This material is based on work supported by NSF Award 2210437, NSF Award 2016136 for the QLCI center Hybrid Quantum Architectures and Networks, the U.S. Department of Energy Office of Science National Quantum Information Science Research Centers and DoE award DE-SC0019465.
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