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We present a gradient-based optimal-control technique for open quantumsystems that utilizes quantum trajectories to simulate the quantum dynamicsduring optimization. Using trajectories allows for optimizing open systems withless computational cost than the regular density matrix approaches in mostrealistic optimization problems. We introduce an improved-sampling algorithmwhich minimizes the number of trajectories needed per optimization iteration.Together with employing stochastic gradient descent techniques, this reducesthe complexity of optimizing many realistic open quantum systems to thecomplexity encountered with closed systems. Our optimizer harnesses automaticdifferentiation to provide flexibility in optimization and to suit thedifferent constraints and diverse parameter regimes of real-life experiments.We utilize the optimizer in a variety of applications to demonstrate how theuse of quantum trajectories significantly reduces the computation complexitywhile achieving a multitude of simultaneous optimization targets. Demonstratedtargets include high state-transfer fidelities despite dissipation, faster gatetimes and maximization of qubit-readout fidelity while maintaining the quantumnon-demolition nature of the measurement and allowing for subsequent fastresonator reset.

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