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Quantum computers drift out of calibration during operation, similar to musical instruments going out of tune, which currently requires stopping the entire computation to recalibrate thousands of control parameters. Researchers integrated reinforcement learning with quantum error correction so that an autonomous agent continuously learns from error detection events and adjusts control parameters in real time, allowing the quantum computer to stay stable during long computations without interruption. This approach matters because useful quantum algorithms must run continuously for extended periods, and traditional physics-based calibration methods hit performance limits as quantum processors become more complex. In experiments, this reinforcement learning method improved logical stability 3.5-fold and reduced logical error rates to record lows, while simulations suggested the approach could scale to much larger quantum systems in the future.
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