This project harnesses ML to develop scalable, feedback‑driven control of complex many-body quantum states. Using information from mid‑circuit measurements it aims to variationally discover interactive circuit architectures that prepare and preserve many‑body states without gradient methods or full classical simulability. We will apply advanced pattern recognition to syndrome and stabilizer measurement data – pushing the limits of fast, ML‑assisted decoding for deformed quantum memories, teleportation, and GHZ state generation in constant‑depth circuits by identifying patterns and extracting the key information that steer complex quantum systems into desired states. Finally, by combining supervised learning for decoding with RL for real‑time feedback control, the project embodies a unified strategy discovery framework that adapts to device‑specific noise characteristics, integrates with state of the art decoding algorithms, and uncovers optimal protocols for fault‑tolerant quantum information processing.