QuantiSpect：用于可扩展表面码量子纠错的结构感知轻量级3D CNN预解码器
QuantiSpect: A Structure-Aware Lightweight 3D CNN Pre-Decoder for Scalable Surface Code Quantum Error Correction
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中文总结 AI 辅助
研究针对大规模容错量子计算实时解码瓶颈，提出QuantiSpect轻量级3D CNN预解码器，通过结构感知因式分解设计，减少参数和计算量，提升解码性能，在不同码距下有显著效果。
中文摘要 AI 辅助
实时解码是大规模容错量子计算的关键瓶颈。基于人工智能的神经预解码器在将残余症候传递给全局解码器之前，先对大多数物理错误进行局部纠正，从而实现亚微秒级延迟。然而，现有架构因密集3D卷积而带来显著开销。我们提出了QuantiSpect，这是一种用于旋转表面码的轻量级3D卷积神经网络（CNN）预解码器，基于Chamberland等人的解码管道构建。关键思想是在每个残差块中用三个并行分支取代密集3D卷积：一个深度空间分支、一个深度时间分支和一个分组时空分支，随后是一个挤压激励通道门。这反映了表面码错误的结构，其中空间和时间症候相关性部分可分离。在统一的4xA100 GPU基准测试中，QuantiSpect在R = 13时与精确基线的感受野匹配，同时使用的参数减少约2.71倍（0.663M对1.80M），每体素卷积MAC减少约2.84倍。它在中等和大码距下与精确的电路级阈值和精度匹配，在d = 13，p = 0.5%时，相对于不相关的PyMatching，逻辑错误率降低高达约1.85倍，在d = 23时，PyMatching解码速度加快高达3.11倍。我们还通过添加块来探索扩大感受野。即使在R = 21时，该模型仅使用1.18M参数，比R = 13的精确基线（1.80M）和R = 17的密集模型（4.22M）都少，尽管其感受野更大。这个扩展变体显著优于精确模型，将电路级阈值提高到约0.80%，并进一步降低逻辑错误率。总之，这两个变体表明，结构感知因式分解设计是解码表面码的一种有效、参数高效的替代密集设计的方法。
英文摘要
Real-time decoding is a critical bottleneck for large-scale fault-tolerant quantum computing. AI-based neural pre-decoders locally correct most physical errors before passing residual syndromes to a global decoder, enabling sub-microsecond latencies. However, existing architectures carry significant overhead from dense 3D convolutions. We present QuantiSpect, a lightweight 3D convolutional neural network (CNN) pre-decoder for the rotated surface code, built on the decoding pipeline of Chamberland et al. The key idea is to replace the dense 3D convolutions with three parallel branches in each residual block: a depthwise spatial branch, a depthwise temporal branch, and a grouped spatio-temporal branch, followed by a squeeze-and-excitation channel gate. This reflects the structure of surface code errors, where spatial and temporal syndrome correlations are partially separable. On a unified 4xA100 GPU benchmark, QuantiSpect matches the receptive field of the Accurate baseline at R=13 while using ~2.71x fewer parameters (0.663M vs 1.80M) and ~2.84x fewer per-voxel convolutional MACs. It matches Accurate's circuit-level threshold and accuracy at moderate and large code distances, reduces the logical error rate by up to ~1.85x relative to uncorrelated PyMatching at d=13, p=0.5%, and speeds up the PyMatching decode by up to 3.11x at d=23. We also explored enlarging the receptive field by adding blocks. Even at R=21, the model uses only 1.18M parameters, fewer than both the R=13 Accurate baseline (1.80M) and the R=17 dense model (4.22M), despite its larger receptive field. This expanded variant significantly outperforms the Accurate model, raising the circuit-level threshold to ~0.80% and further reducing the logical error rate. Together, both variants show that a structure-aware factorized design is an effective, parameter-efficient alternative to a dense one for decoding the surface code.
