Reinforcement learning enables continuous self-calibration in quantum error correction

A new quantum computing method uses reinforcement learning to continuously calibrate quantum error correction during computation, improving logical stability and achieving record low error rates. This approach repurposes error detection signals as learning inputs for an AI agent, enabling the quantum computer to self-correct and adapt to environmental changes without interrupting calculations. Experiments showed a 3.5-fold improvement in stability against injected drift. The framework is scalable to large quantum systems and applicable to various qubit technologies, paving the way for more robust and efficient quantum computation.