Autonomous calibration of quantum processors with Bayesian filtering

Autonomous calibration of emerging many qubit quantum processors will be essential to achieving and maintaining sufficiently small error rates to enable fault tolerant quantum computation. Classic nonlinear Kalman filters provide one possible framework, and we have found that quantum gate set tomography is amenable to estimation with the extended and sigma point filters, achieving fits comparable with maximum likelihood estimation in simulation. This compatibility indicates a promising route towards online and adaptive calibration of quantum processors using existing techniques in classical control theory. We incorporate the effect of controls in a filter’s dynamic state model to derive an optimal linear quadratic regulator for a quantum gate set. After selecting a noise model and a control map for a given hardware, our regulator may be deployed and called periodically for gate tuneup or continuously for drift compensation. Improvements in metrics like average gate set infidelity or randomized benchmarking rates verify successful calibration cycles. We are constructing a low level, hardware agnostic calibration algorithm for the next generation of quantum computers.