The system was put in charge of two logical qubits hosted on a calibrated system. The two were using different error correction schemes (a surface code and a color code). These were set in a specific state, and the error-correction system was then used with and without reinforcement-learning-driven corrections. Having the system active led to a 20 percent increase in the ability to detect and correct errors in the logical qubits.
Going real time
The limitation of this approach is that it works only if the drift keeps the system reasonably close to the state the system was trained in. The corrections that might bring things back into alignment
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