Quantum Optimal Control from a Robotics Perspective

We are presenting our work applying ideas from robotic control to quantum optimal control (QOC). In our recent paper, Direct Collocation for Quantum Optimal Control, we showed how one of these ideas can be applied with state-of-the-art results to several systems in simulation and also on an experimental 3D circuit cavity quantum electrodynamics system. We have also developed easy-to-use software for these methods available as an open-source Julia package. To fully bridge the gap between simulation and experiment, and allow for automated calibration of pulses, we have embedded our direct collocation method within an iterative learning control algorithm, which has the potential to lower calibration time for complex control pulses.