Research & academia
Towards a quantum computer that learns from its errors
A reinforcement-learning agent uses quantum error detections not only to correct errors but also to steer thousands of control parameters during computation, counteracting drift without stopping the run.
Why it matters: The method targets a bottleneck for long-running quantum algorithms: calibration currently interrupts computation. The hardware experiment supports improved stability under artificial drift; the larger-system scaling result comes from simulation, not a large-processor demonstration.