Scalable Neural Decoders for Practical Fault-Tolerant Quantum Computation

TL;DR

This paper presents a neural network decoder for quantum error correction that significantly improves error suppression and throughput, enabling more practical fault-tolerant quantum computing.

Contribution

Introduction of a convolutional neural network decoder exploiting geometric code structure, achieving higher error suppression and real-time performance in quantum error correction.

Findings

Achieves logical error rates up to 17 times lower than existing decoders.

Reaches logical error rates around 10^{-10} at 0.1% physical error rate.

Provides well-calibrated confidence estimates reducing overhead.

Abstract

Quantum error correction (QEC) is essential for scalable quantum computing. However, it requires classical decoders that are fast and accurate enough to keep pace with quantum hardware. While quantum low-density parity-check codes have recently emerged as a promising route to efficient fault tolerance, current decoding algorithms do not allow one to realize the full potential of these codes in practical settings. Here, we introduce a convolutional neural network decoder that exploits the geometric structure of QEC codes, and use it to probe a novel "waterfall" regime of error suppression, demonstrating that the logical error rates required for large-scale fault-tolerant algorithms are attainable with modest code sizes at current physical error rates, and with latencies within the real-time budgets of several leading hardware platforms. For example, for the $[144, 12, 12]$ Gross code, the decoder achieves logical error rates up to $\sim 17$x below existing decoders - reaching logical error rates $\sim 10^{-10}$ at physical error $p=0.1\%$ - with 3-5 orders of magnitude higher throughput. This decoder also produces well-calibrated confidence estimates that can significantly reduce the time overhead of repeat-until-success protocols. Taken together, these results suggest that the space-time costs associated with fault-tolerant quantum computation may be significantly lower than previously anticipated.