Real-time Surface-Code Error Correction Using an FPGA-based Neural-Network Decoder
Real-time Surface-Code Error Correction Using an FPGA-based Neural-Network Decoder
Xiaohan Yang, Xuandong Sun, Zhiyi Wu, Jiawei Zhang, Ji Jiang, Xiayu Linpeng, Yuxuan Zhou, Ji Chu, Jingjing Niu, Youpeng Zhong, Song Liu, Dapeng Yu
AbstractQuantum error correction (QEC) is essential for achieving low error rates required for fault-tolerant quantum computation. In stabilizer-based codes such as the surface code, errors are inferred from repeated syndrome measurements and corrected by a classical decoder. To prevent error accumulation, decoding must be performed with both high throughput and low latency to keep pace with the QEC cycle and enable real-time feedback for universal logical operations. Here we report a hardware-integrated control architecture featuring an FPGA-based neural-network (NN) decoder and experimentally demonstrate real-time surface-code (distance-3) QEC on a superconducting quantum processor. The system achieves a deterministic closed-loop latency of 550 ns, including 124 ns for NN decoding, enabling feedback corrections within a 1.25 us QEC cycle. We show that real-time decoding and feedback correction achieve logical performance comparable to offline decoding while maintaining robustness against varying error conditions. We further demonstrate mid-circuit feedback correction in non-Clifford logical circuits, where Pauli-frame updating alone becomes insufficient. Our results establish a low-latency hardware architecture for embedded QEC control and provide a pathway towards scalable fault-tolerant quantum computing systems.