  We present a gradient-based optimal-control technique for open quantum
systems that utilizes quantum trajectories to simulate the quantum dynamics
during optimization. Using trajectories allows for optimizing open systems with
less computational cost than the regular density matrix approaches in most
realistic optimization problems. We introduce an improved-sampling algorithm
which minimizes the number of trajectories needed per optimization iteration.
Together with employing stochastic gradient descent techniques, this reduces
the complexity of optimizing many realistic open quantum systems to the
complexity encountered with closed 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.
We utilize the optimizer 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, faster gate
times and maximization of qubit-readout fidelity while maintaining the quantum
non-demolition nature of the measurement and allowing for subsequent fast
resonator reset.
