Google Uses Error Signals to Retune Willow's Quantum Controls
A reinforcement-learning experiment improves stability under injected drift and fine-tunes an already calibrated processor.
Google’s Sycamore processor, Willow’s predecessor, used as company hardware context. It is not a Willow chip. Image credit“Sycamore Quantum Chip” by Coldupnorth, CC BY-SA 4.0. · https://creativecommons.org/licenses/by-sa/4.0/
Google researchers are using Willow's quantum error-detection events as reinforcement-learning feedback to adjust the processor's control parameters. The peer-reviewed hardware experiment explores whether keeping calibration running during computation could reduce interruptions caused by hardware drift.
The paper reports a 2.4-fold improvement in logical-error-rate stability under injected drift, rising to 3.5-fold with additional decoder steering. In a separate test, fine-tuning an already calibrated processor reduced logical errors by about 20%.
Those results have different conditions: the 3.5-fold figure includes decoder steering whose reported implementation faces real-time scaling limitations, while the large-code results come from simulations. Further tests of sustained operation under natural drift and scalable decoder feedback would clarify how far those gains extend.