
Reinforcement Learning Keeps Quantum Error Correction Running Without Constant Recalibration
One of the most persistent challenges in quantum computing is drift: the control parameters that keep a quantum processor running at peak performance shift over time due to temperature fluctuations, material aging, and other environmental factors. The standard solution is to stop the computation, run calibration routines, and restart, a process that wastes time and […]










