Gradient-based optimal control of open systems using quantum trajectories and automatic differentiation

We present a gradient-based optimal-control technique for open quantum systems that utilizes quantum trajectories. Using trajectories allows for optimizing open systems with less computational cost than the regular density matrix approaches. In addition, we propose an improved sampling algorithm to minimize the required number of trajectories needed per optimization iteration. Together with employing stochastic gradient descent techniques, this reduces the complexity of optimizing realistic open quantum systems. Our optimizer harnesses automatic differentiation to provide flexibility in optimization and to suit the different constraints and diverse parameter regimes of real-life experiments. The optimizer is utilized in a variety of applications to demonstrate how the use of quantum trajectories significantly reduces the computation complexity while achieving a multitude of simultaneous optimization targets. Demonstrated targets include high state transfer fidelities despite dissipation, and maximizing the readout fidelities of a qubit while maintaining the quantum non-demolition nature of the measurement and allowing for subsequent fast resonator reset.