Scalable Neural Decoders for Practical Real-Time Quantum Error Correction

• URL: http://arxiv.org/abs/2510.22724v1
• Date: Sun, 26 Oct 2025 15:49:46 GMT
• Title: Scalable Neural Decoders for Practical Real-Time Quantum Error Correction
• Authors: Changwon Lee, Tak Hur, Daniel K. Park,
• Abstract summary: We introduce and evaluate a textitMamba-based decoder, a state-space model with $mathcalO(d2)$ complexity.<n>Mamba decoders offer a compelling balance between speed and accuracy, making them a promising architecture for scalable, real-time quantum error correction.
• Score: 1.474723404975345
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-time, scalable, and accurate decoding is a critical component for realizing a fault-tolerant quantum computer. While Transformer-based neural decoders such as \textit{AlphaQubit} have demonstrated high accuracy, the computational complexity of their core attention mechanism, which scales as $\mathcal{O}(d^4)$ with code distance $d$, results in decoding speeds insufficient for practical real-time applications. In this work, we introduce and evaluate a \textit{Mamba}-based decoder, a state-space model with $\mathcal{O}(d^2)$ complexity. In memory experiments using Sycamore hardware data, our Mamba decoder matches the performance of its Transformer-based counterpart, providing that its superior efficiency does not come at the cost of performance. Crucially, in simulated real-time scenarios that account for decoder-induced noise, the Mamba decoder significantly outperforms the Transformer, exhibiting a higher error threshold of $0.0104$ compared to $0.0097$. These results demonstrate that Mamba decoders offer a compelling balance between speed and accuracy, making them a promising architecture for scalable, real-time quantum error correction.

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