Willow retuning was tested under injected drift
A dated, source-bounded reading of the original report.
Willow retuning was tested under injected drift
- Reported
- Sivak and colleagues report reinforcement-learning control that reduces variation in logical error rates under deliberately injected drift.
- Supported observation
- Control steering improves stability 2.4-fold against a fixed policy; adding decoder steering raises this to 3.5-fold. Stability here refers to the standard deviation of logical error rates.
- Conditions
- The hardware experiment uses short quantum-memory runs on Willow with states prepared again for each shot. The decoder-steering method also estimates logical errors. The drift comparison uses control parameters fixed at their initial calibration.
Not established
This does not demonstrate uninterrupted execution of a long algorithm. The reported decoder steering has real-time scaling limitations; large-code scaling and continuous steering are studied in simulation.
- Reinforcement learning control of quantum error correctionNature · Peer-reviewed primary research paper · Source published Source checked
What would change this record?
Longer hardware runs with scalable feedback and explicit accounting for exploration-induced errors.
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