A Google Quantum AI team demonstrates a reinforcement-learning agent that continuously tunes thousands of control parameters on a quantum processor, using error-detection events as a live learning signal. With a sparse-factor-graph surrogate objective, the AI localizes optimization to tiny neighborhoods, allowing scalable fault-tolerance without pausing computations. The result—3.5× improvement in logical stability against environmental drift and beating expert calibration by about 20%—points to a future where large quantum machines can run long-running simulations for chemistry and medicine. We unpack how continuous learning can stabilize fragile quantum hardware and what this could mean for AI-assisted self-healing of complex systems.
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