Data-Driven Qubit Characterization and Optimal Control using Deep Learning

Making quantum computing practically viable requires the optimization of high-fidelity gate control pulses. One approach is the generation of control pulses based on numerical optimization. However, purely model-based approaches for offline-optimization face the difficulty that the qubit dynamics depend on many aspects of the experimental setup and device, many of which are difficult to characterize. Alternatively, closed-loop optimization schemes like Gate Set Calibration (GSC) [1], a protocol using linearized Gate Set Tomography, can be used for optimizing gates directly on the experiment. However, such approaches do not support the use of gradient-based optimization algorithms, because the estimation of gradients by finite differences can require too many measurements.

I will present a hybrid data driven method to address this problem, in which a surrogate model is obtained by training a RNN to predict the observed measurement outcomes after executing random pulses. Then, control pulses are obtained by using an GSC [1] inspired loss function as a proxy for the infidelity. Thus, the dynamics of the sampled device are represented with minimal prior models and the surrogate model provides methods to efficiently calculate gradients. This approach is verified in simulation.

[1] https://doi.org/10.1103/PhysRevApplied.13.044071