Monitoring Fast Superconducting Qubit Dynamics Using a Neural Network
Abstract
Weak measurements of a superconducting qubit produce noisy voltage signals that are weakly correlated with the qubit state. To recover individual quantum trajectories from these noisy signals, traditional methods require slow qubit dynamics and substantial prior information in the form of calibration experiments. Monitoring rapid qubit dynamics, e.g., during quantum gates, requires more complicated methods with increased demand for prior information. Here, we experimentally demonstrate an alternative method for accurately tracking rapidly driven superconducting qubit trajectories that uses a long shortterm memory (LSTM) artificial neural network with minimal prior information. Despite few training assumptions, the LSTM produces trajectories that include qubitreadout resonator correlations due to a finite detection bandwidth. In addition to revealing rotated measurement eigenstates and a reduced measurement rate in agreement with theory for a fixed drive, the trained LSTM also correctly reconstructs evolution for an unknown drive with rapid modulation. Our work enables new applications of weak measurements with faster or initially unknown qubit dynamics, such as the diagnosis of coherent errors in quantum gates.
 Publication:

Physical Review X
 Pub Date:
 July 2022
 DOI:
 10.1103/PhysRevX.12.031017
 arXiv:
 arXiv:2108.12023
 Bibcode:
 2022PhRvX..12c1017K
 Keywords:

 Quantum Physics
 EPrint:
 19 pages, 13 figures