AI BriefingGoogle Unveils RL-Based Quantum Error Correction
Key point
Google published a technique in Nature that uses reinforcement learning to correct quantum computer errors in real time.
Details
A study published in Nature by Google Quantum AI and DeepMind proposes a framework that utilizes error detection signals arising during the quantum error correction (QEC) process as training signals for a reinforcement learning (RL) agent.
While existing methods have the limitation of requiring calculations to be halted for error correction, this study adjusts control parameters in real time without interrupting calculations to offset drift. Validated on Google's superconducting processor Willow, the results showed a 3.5x improvement in logical stability under artificial drift conditions and an additional 20% suppression of the logical error rate (LER) compared to manual correction by experts.
This approach realizes 'model-free in-context fine-tuning' necessary to maintain performance below the QEC threshold, which is expected to contribute to enhancing the long-term runnability of large-scale quantum processors.
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