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 short-term memory (LSTM) artificial neural network with minimal prior information. Despite few training assumptions, the LSTM produces trajectories that include qubit-readout 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.
- Received 6 September 2021
- Revised 21 April 2022
- Accepted 17 June 2022
DOI:https://doi.org/10.1103/PhysRevX.12.031017
Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.
Published by the American Physical Society
Physics Subject Headings (PhySH)
Popular Summary
Weak measurements of a quantum system allow us to monitor a quantum state in real time with only a small disturbance. Finding the quantum state from a series of weak measurements typically involves a “quantum filter” derived from basic laws of quantum mechanics. This traditional weak measurement approach works well if the quantum state changes slowly compared with the detector response time. However, if the qubit changes rapidly, traditional methods that reconstruct the quantum state fail because the detector affects our best estimate of the quantum state. In our experiment, we use weak measurements to monitor fast dynamics of a superconducting qubit coupled to a readout resonator.
Instead of using a traditional method to monitor the qubit state, we develop a new method with a long short-term memory neural network, which learns the quantum mechanics responsible for the state trajectories by itself. The long short-term memory neural network also learns an unexpected correction to the standard quantum filter, which is most clearly visible in the stochastic measurement disturbance of the fast qubit trajectories. Our newly developed theory shows that this correction can be well explained by the memory effect of the detector.
With our ability to accurately track fast qubit dynamics, we expect to see new applications of weak measurements such as diagnosing qubit gates in quantum processors and continuous measurements for quantum error correction.
