Learning to Decode in Parallel: Self-Coordinating Neural Network for Real-Time Quantum Error Correction

2026-01-14

AI Summary

Existing neural network decoders lack parallelism, hindering their ability to meet the real-time, high-throughput decoding demands of superconducting quantum computing. This work proposes a recurrent Transformer architecture tailored for sliding-window parallel decoding, which generates globally consistent labels through local corrections and achieves cross-window self-consistency by jointly training multiple window types. For the first time, this approach attains practical-level throughput while preserving state-of-the-art decoding accuracy, thereby overcoming the real-time application bottleneck inherent in AlphaQubit-style decoders. Experimental validation on the Zuchongzhi 3.2 superconducting quantum processor demonstrates high-fidelity decoding for distance-7 surface codes; furthermore, a single TPU v6e chip accomplishes one round of decoding for a distance-25 surface code within one microsecond.

Technology Category

• Machine Learning: Quantum Machine Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

Fast, reliable decoders are pivotal components for enabling fault-tolerant quantum computation (FTQC). Neural network decoders like AlphaQubit have demonstrated potential, achieving higher accuracy than traditional human-designed decoding algorithms. However, existing implementations of neural network decoders lack the parallelism required to decode the syndrome stream generated by a superconducting logical qubit in real time. Moreover, integrating AlphaQubit with sliding window-based parallel decoding schemes presents non-trivial challenges: AlphaQubit is trained solely to output a single bit corresponding to the global logical correction for an entire memory experiment, rather than local physical corrections that can be easily integrated. We address this issue by training a recurrent, transformer-based neural network specifically tailored for parallel window decoding. While it still outputs a single bit, we derive training labels from a consistent set of local corrections and train on various types of decoding windows simultaneously. This approach enables the network to self-coordinate across neighboring windows, facilitating high-accuracy parallel decoding of arbitrarily long memory experiments. As a result, we overcome the throughput bottleneck that previously precluded the use of AlphaQubit-type decoders in FTQC. Our work presents the first scalable, neural-network-based parallel decoding framework that simultaneously achieves SOTA accuracy and the stringent throughput required for real-time quantum error correction. Using an end-to-end experimental workflow, we benchmark our decoder on the Zuchongzhi 3.2 superconducting quantum processor on surface codes with distances up to 7, demonstrating its superior accuracy. Moreover, we demonstrate that, using our approach, a single TPU v6e is capable of decoding surface codes with distances up to 25 within 1us per decoding round.

Problem

• quantum error correction
• parallel decoding
• neural network decoder
• fault-tolerant quantum computation
• real-time decoding

Innovation

• parallel decoding
• neural network decoder
• quantum error correction
• self-coordinating
• real-time FTQC