Linear-quadratic regulation of drift in quantum processor

Rapid calibration and recalibration of a quantum processor in the presence of drift will be essential to achieve and maintain error rates below fault tolerance thresholds. Towards this goal, we are adapting the classical linear-quadratic regulator (LQR) within the context of quantum devices to support a streaming calibration approach that uses active feedback to rapidly tune device settings on-line. One starts by identifying an appropriate error model for a device and an observation function such as repeated sequences of gates, as in gate set tomography and robust phase estimation. Next, one defines a quadratic cost function to penalize deviations from the target performance. The LQR provides the optimal control law that minimizes this cost function. Experimental implementation of LQR policies require very little computational resources and can be embedded directly on classical control hardware for low-latency, real-time control of drift in current devices.