Improved Belief Propagation Decoding Algorithms for Surface Codes

TL;DR

This paper introduces novel belief propagation algorithms inspired by machine learning to significantly improve decoding accuracy for surface codes in quantum error correction, achieving high precision and efficiency.

Contribution

It proposes Momentum-BP, AdaGrad-BP, and EWAInit-BP algorithms that enhance belief propagation decoding accuracy without post-processing, with EWAInit-BP offering 1-3 orders of magnitude improvement.

Findings

EWAInit-BP outperforms traditional BP by 1-3 orders of magnitude.

EWAInit-BP achieves high accuracy without post-processing.

Algorithms maintain $O(1)$ time complexity under parallel implementation.

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 ensuring quantum error correction works effectively. 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. Inspired by machine learning optimization techniques, we first propose Momentum-BP and AdaGrad-BP to reduce oscillations in message updating, breaking the trapping sets of surface codes. 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 implementation and high accuracy of EWAInit-BP make it a promising candidate for high-precision real-time decoders.