Self-attention U-Net decoder for toric codes

TL;DR

This paper introduces a self-attention U-Net quantum decoder for toric codes that surpasses traditional decoders in noisy environments, enhancing quantum error correction efficiency and scalability.

Contribution

The paper presents a novel self-attention U-Net decoder for toric codes, improving error correction performance and scalability in quantum computing.

Findings

Lower logical error rates than MWPM decoder.

Achieves a high threshold of 0.231 in biased noise environments.

Scalable with transfer learning for different code distances.

Abstract

In the NISQ era, one of the most important bottlenecks for the realization of universal quantum computation is error correction. Stabiliser code is the most recognizable type of quantum error correction code. A scalable efficient decoder is most desired for the application of the quantum error correction codes. In this work, we propose a self-attention U-Net quantum decoder (SU-NetQD) for toric code, which outperforms the minimum weight perfect matching decoder, especially in the circuit level noise environments. Specifically, with our SU-NetQD, we achieve lower logical error rates compared with MWPM and discover an increased trend of code threshold as the increase of noise bias. We obtain a high threshold of 0.231 for the extremely biased noise environment. The combination of low-level decoder and high-level decoder is the key innovation for the high accuracy of our decoder. With transfer learning mechanics, our decoder is scalable for cases with different code distances. Our decoder provides a practical tool for quantum noise analysis and promotes the practicality of quantum error correction codes and quantum computing.