Continuously Calibrating Quantum Error Correction with Reinforcement Learning
bravo_abad · x · 2026-07-14
During long-term operation of quantum computers, underlying hardware parameters drift over time, and traditional interrupt-based recalibration cannot meet the needs of future algorithms requiring continuous operation for months.
Researchers propose transforming the error correction process itself into a learning loop: using detection events originally sent only to the decoder as feedback signals for a reinforcement learning agent to continuously fine-tune the processor's physical control parameters. To address the prohibitive cost of directly optimizing the logical error rate, the authors defined a surrogate objective based on local detection rates, utilizing sparse factor graph structures to achieve scalable policy gradient learning.
Experiments on the Google Willow superconducting processor show that this RL fine-tuning reduces the logical error rate by roughly 20% on top of traditional calibration. In tests with injected hardware drift, continuous bootstrapping reduced the average logical error rate by 24% and improved stability by 2.4x (with decoder adaptation, the reduction and stability improvements reached 31% and 3.5x, respectively). Simulations show that this method can scale to a distance-15 code with nearly 40,000 control parameters, with optimization speed largely unaffected by system size.
This closed-loop control strategy is applicable not only to quantum computing but also holds future promise for scientific research platforms requiring long-term autonomous operation, such as drug discovery and materials science.
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