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The adaptive decision-making strategy for surface code decoding
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AI
DOI：10.1088/2058-9565/ae3f4e.png)
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Abstract
En 中文
Achieving large-scale real-time decoding requires efficient and accurate decoders. However, conventional decoding strategies for surface codes struggle to balance speed with accuracy. In this work, we propose the adaptive decision-making (ADM) strategy, which optimizes the local decisions through intermediate information during the decoding process, while maintaining rapid decoding. We compare the performance of different real-time strategies by integrating them into the union-find (UF) decoding framework. Numerical simulations under different noises show that our ADM-UF decoder significantly improves the threshold, outperforming its highly relevant competitors. Empirical evidence confirms that ADM-UF achieves an almost-linear complexity, ensuring real-time decoding for large-scale quantum codes. These results validate the efficacy of our ADM strategy.
Keywords:
quantum error correction
fault-tolerant quantum computing
surface code
decoding
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1.4K
Citations:
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