Scalable Neural Decoders for Practical Real-Time Quantum Error Correction
This addresses a critical bottleneck for fault-tolerant quantum computing by enabling faster, scalable decoders, though it is incremental as it builds on existing neural decoder approaches.
The paper tackled the problem of high computational complexity in Transformer-based neural decoders for quantum error correction, which limits real-time scalability, and introduced a Mamba-based decoder that reduces complexity from O(d^4) to O(d^2) while matching or outperforming Transformer accuracy, achieving a higher error threshold of 0.0104 vs. 0.0097 in simulations.
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.