Feedback-Based Steering for Quantum State Preparation

State preparation is an essential component in quantum information science. A recently developed steering protocol utilizes a sequence of generalized measurements on a detector to steer a quantum system towards a desired state. However, it is designed as an open-loop technique that requires accurate modeling of the overall quantum system and can be prone to errors. To address this challenge, we propose a closed-loop control technique that introduces feedback to the steering protocol, providing robustness to noise and faster state convergence. We introduce two strategies for feedback: (1) a gradient-based active steering protocol that changes the detector-system coupling conditioned on the detector's readout and (2) tuning the fixed detector-system coupling via model-free reinforcement learning. We study the effectiveness of these strategies under various noise models, including both incoherent and decoherent noise, and discuss potential applications in quantum technologies.