Reinforcement Learning Control of Quantum Error Correction

The promise of fault-tolerant quantum computing is challenged by environmental drift that relentlessly degrades the quality of quantum operations. The current-day solution, halting the entire quantum computation for recalibration, is unsustainable for the long runtimes of the future algorithms. We address this challenge by unifying calibration with computation, granting the quantum error correction process a dual role: its error detection events are not only used to correct the logical quantum state, but are also repurposed as a learning signal, teaching a reinforcement learning agent to continuously steer the physical control parameters and stabilize the quantum system during the computation. We experimentally demonstrate this framework on a Willow superconducting quantum processor developed by Google Quantum AI, achieving state-of-the-art performance and stability. Our work enables a new paradigm: a quantum computer that learns to self-improve directly from its errors and never stops computing.