Reinforcement learning for calibrating quantum processors

As quantum processors grow larger, manual calibration of logic operations quickly becomes impossible. Instead, automated solutions are required that can rapidly identify miscalibrated control parameters and correct them. Reinforcement learning has demonstrated remarkable success at automating a wide range of tasks through trial and error and feedback, providing nearly turn-key methods that efficiently balance parameter space exploration with exploitation of gained knowledge. In this talk, I will outline a simple example of how calibration of a quantum device can be cast as an environment/agent/reward problem that yields to techniques from deep reinforcement learning. Our approach probes the calibration state of the system using long, periodic gate sequences that are maximally sensitive to coherent errors---those that are most susceptible to calibration. I demonstrate the performance of this technique in simulation. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.