Gradient Ascent Pulse Engineering with Feedback

Abstract

Efficient approaches to quantum control and feedback are essential for quantum technologies, from sensing to quantum computation. Open-loop 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. In this work, we introduce feedback-GRAPE, which borrows some concepts from model-free reinforcement learning 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 considering various scenarios based on cavity QED setups. Our method yields interpretable feedback strategies for state preparation and stabilization in the presence of noise. Our 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.

31 pages, 16 figures

References in corpus (4)

• Quantum feedback control of a superconducting qubit: Persistent Rabi oscillations
• Progressive field-state collapse and quantum non-demolition photon counting
• Gradient-based optimal control of open quantum systems using quantum trajectories and automatic differentiation
• Control of Stochastic Quantum Dynamics by Differentiable Programming

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• Tackling the Challenges of Adding Pulse-level Support to a Heterogeneous HPCQC Software Stack: MQSS Pulse
• CDJ-Pontryagin Optimal Control for General Continuously Monitored Quantum Systems