Latency-Constrained Hardware-Aware Quantum Error Correction Co-Design with Adaptive Confidence-Gated Neural Decoding for the Rotated Surface Code

2026-07-09 · Source: cs.LG updates on arXiv.org · Field: Science & Research — Physical Sciences & Chemistry, Mathematics & Computational Sciences, Engineering & Applied Sciences · Depth: Expert, extended

Summary

A new adaptive confidence-gated decoding framework has been developed for the rotated surface code, addressing the real-time decoding bottleneck in quantum error correction. This two-stage inference system employs a lightweight feed-forward neural network for rapid decoding of most syndrome measurements. Only low-confidence predictions, specifically 3.3%–6.2% of syndromes, are escalated to a more robust Minimum-Weight Perfect Matching (MWPM) refinement stage. Benchmarked on rotated surface codes with distances d∈{3,5,7,9,11} under circuit-level depolarising noise using the Stim simulator, the framework demonstrated significant accuracy improvements. At a confidence threshold of 0.95, logical accuracy rose from 99.21% for the neural-only baseline to 99.81%. The neural fast path achieved throughput near 4.6×10⁵ samples s-1 at batch size 512 on commodity CPU hardware, suggesting it is not the primary throughput bottleneck beyond d=7. This framework represents a key component of a broader hardware-aware QEC co-design architecture.

Key takeaway

For AI Hardware Engineers and Research Scientists designing fault-tolerant quantum computing systems, you should consider implementing adaptive, confidence-gated decoding. This approach allows your fast neural decoders to handle most syndromes efficiently, while escalating only a small fraction of low-confidence cases to more accurate, albeit slower, exact decoders. This strategy significantly boosts overall logical accuracy without incurring prohibitive average latency, enabling more robust real-time quantum error correction.

Key insights

Adaptive, confidence-gated neural decoding significantly improves quantum error correction accuracy by escalating only low-confidence syndromes.

Principles

• Logical error rates are dominated by rare, hard-to-decode syndromes.
• Decoder confidence can effectively identify error-prone syndromes.
• QEC co-design requires joint optimization of code, decoder, and hardware.

Method

A two-stage inference process: a feed-forward neural network performs fast-path decoding, escalating low-confidence syndromes (below a tunable threshold τ) to a Minimum-Weight Perfect Matching (MWPM) refinement stage.

In practice

• Implement a two-tier decoder with a fast neural network.
• Use confidence scores to route hard syndromes to a slower, exact decoder.
• Benchmark decoder performance across code distance, noise, and batch size.

Topics

• Quantum Error Correction
• Rotated Surface Code
• Neural Decoders
• Minimum-Weight Perfect Matching
• Hardware-Aware Co-Design
• Real-Time Decoding

Code references

• Sumitchongder/adaptive-qec-decoder

Best for: AI Scientist, AI Hardware Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.LG updates on arXiv.org.