Improved Belief Propagation Decoding Algorithms for Surface Codes (2407.11523v3)

Abstract: Quantum error correction is crucial for universal fault-tolerant quantum computing. Highly accurate and low-time-complexity decoding algorithms play an indispensable role in making sure quantum error correction works. Among existing decoding algorithms, belief propagation (BP) is notable for its nearly linear time complexity and general applicability to stabilizer codes. However, BP's decoding accuracy without post-processing is unsatisfactory in most situations. This article focuses on improving the decoding accuracy of BP over GF(4) for surface codes. We first propose Momentum-BP and AdaGrad-BP, inspired by machine learning optimization techniques, to reduce oscillation in message updating and break the symmetric trapping sets. We further propose EWAInit-BP, which adaptively updates initial probabilities and provides a 1 to 3 orders of magnitude improvement over traditional BP for planar surface code, toric code, and XZZX surface code without any post-processing method, showing high decoding accuracy even under parallel scheduling. The theoretical $O(1)$ time complexity under parallel scheduling and high accuracy of EWAInit-BP make it a promising candidate for high-precision real-time decoders. Meanwhile, the ideas of the Momentum-BP, AdaGrad-BP and EWAInit-BP provide promising approaches to improve the decoding accuracy of BP to get rid of its reliance on post-processing.