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 L28: Fluxonium Qubits
8:00 AM–11:00 AM,
Wednesday, March 17, 2021
Sponsoring
Unit:
DQI
Chair: David Zajac, IBM TJ Watson Research Center
Abstract: L28.00012 : Machine Learning Approach to Characterization of Multiple Fluxonium Qubits*
10:36 AM–10:48 AM
Live
Presenter:
Yinqi Chen
(Department of Physics and Wisconsin Quantum Institute, University of Wisconsin - Madison)
Authors:
Yinqi Chen
(Department of Physics and Wisconsin Quantum Institute, University of Wisconsin - Madison)
Maxim G Vavilov
(Department of Physics and Wisconsin Quantum Institute, University of Wisconsin - Madison)
Hamiltonian diagonalization for each new set of energy parameters is viable for a small number of qubits. For a larger system of qubits, such fitting becomes inefficient. Here we propose a machine learning approach by training a neural network that takes the measured 0-1 transition energies at several external flux values and predicts parameters EC, EL, and EJ for each uxonium qubit. We demonstrate that such a prediction model can be generalized for many non-interacting or weakly interacting fluxonium qubits. This approach also allows us to identify transitions between higher energy states correctly. Analyzing energy parameters of a large number of fluxoniums on a chip will help to improve the fabrication of fluxonium processors.
*Research was supported by a QIS Award funded by U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award No. DE-SC0019449.
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