CIM-Based Parallel Fully FFNN Surface Code High-Level Decoder for Quantum Error Correction

Hao Wang, Erjia Xiao, Songhuan He, Zhongyi Ni, Lingfeng Zhang, Xiaokun Zhan, Yifei Cui, Jinguo Liu, Cheng Wang, Zhongrui Wang, Renjing Xu

Abstract: In all types of surface code decoders, fully neural network-based high-level decoders offer decoding thresholds that surpass decoder-Minimum Weight Perfect Matching (MWPM), and exhibit strong scalability, making them one of the ideal solutions for addressing surface code challenges. However, current fully neural network-based high-level decoders can only operate serially and do not meet the current latency requirements (below 440 ns). To address these challenges, we first propose a parallel fully feedforward neural network (FFNN) high-level surface code decoder, and comprehensively measure its decoding performance on a computing-in-memory (CIM) hardware simulation platform. With the currently available hardware specifications, our work achieves a decoding threshold of 14.22%, and achieves high pseudo-thresholds of 10.4%, 11.3%, 12%, and 11.6% with decoding latencies of 197.03 ns, 234.87 ns, 243.73 ns, and 251.65 ns for distances of 3, 5, 7 and 9, respectively.

External IDs:dblp:conf/date/WangXHNZZCLWWX25