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Abstract

Efficient approaches to quantum control and feedback are essential for quantum technologies, from sensing to quantum computation. Pure control tasks have been successfully solved using optimization techniques, including methods like gradient-ascent pulse engineering (GRAPE) , relying on a differentiable model of the quantum dynamics. For feedback tasks, such methods are not directly applicable, since the aim is to discover strategies conditioned on measurement outcomes. There, model-free reinforcement learning (RL) has recently proven a powerful new ansatz. What is missing is a way to combine the best of both approaches for scenarios that go beyond weak measurements. In this work, we introduce feedback-GRAPE, which borrows concepts from model-free RL to incorporate the response to strong stochastic (discrete or continuous) measurements, while still performing direct gradient ascent through the quantum dynamics. We illustrate its power on a Jaynes-Cummings model with feedback, where it yields interpretable feedback strategies for state preparation and stabilization in the presence of noise. This approach could be employed for discovering strategies in a wide range of feedback tasks, from calibration of multi-qubit devices to linear-optics quantum computation strategies, quantum-enhanced sensing with adaptive measurements, and quantum error correction.