  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.
