Robust Adaptive Quantum Feedback Under Practical Uncertainties- A Lightweight Approach

Quantum systems are highly sensitive to timing jitter, measurement noise, and partial observability, which degrade feedback control performance. We present a lightweight adaptive quantum feedback strategy inspired by Deep Deterministic Policy Gradient (DDPG), but designed to operate without full reinforcement learning training. The method adaptively updates control gains using filtered partial measurements and scalar rewards to enhance fidelity under uncertainty. We describe the control formulation, motivate its relevance to near-term devices, and outline future directions for simulation and implementation.