adaptive-confidence-gated-qec-decoding

name	adaptive-confidence-gated-qec-decoding
description	Two-stage adaptive confidence-gated neural decoding framework for quantum error correction — lightweight neural fast-path with high-confidence fallback to classical refinement. Use when designing real-time QEC decoding, neural-classical hybrid inference, confidence-gated routing, latency-constrained quantum control, or hardware-aware QEC co-design. Activation: confidence gating, two-stage decoding, neural decoder, QEC latency, surface code, MWPM refinement, accuracy-latency tradeoff, rotated surface code, adaptive inference, hardware-aware
metadata	{"arxiv_id":"2607.05814","published":"2026-07-07","authors":"Sumit Chongder","category":"quant-ph, cs.ET, cs.LG","tags":["quantum-error-correction","neural-decoding","confidence-gating","surface-code","hardware-aware","two-stage-inference","latency-optimization"]}

Adaptive Confidence-Gated QEC Decoding

Two-stage inference framework that routes syndrome measurements through a lightweight neural fast-path, escalating only low-confidence predictions to classical MWPM refinement.

Core Architecture

Syndrome Measurement → Neural Fast-Path (FFNN)
                         ├─ High-confidence (>threshold) → Accept
                         └─ Low-confidence (<threshold) → MWPM Refinement

Key Results (arXiv:2607.05814)

• Routing efficiency: Only 3.3%–6.2% of syndromes escalated to refinement at confidence threshold 0.95
• Accuracy improvement: 99.21% (neural-only) → 99.81% (with refinement)
• Throughput: ~4.6×10⁵ samples/s on commodity CPU (batch size 512)
• Scalability: Neural path not the bottleneck beyond d=7

Methodology

1. Train lightweight FFNN on syndrome-to-correction mapping for target code distance
2. Calibrate confidence threshold on validation set — sweep accuracy vs latency trade-off
3. Deploy two-stage decoder: neural fast-path for majority, MWPM fallback for edge cases
4. Benchmark: logical accuracy, per-shot latency, throughput, resource scaling across distances d∈{3,5,7,9,11}

Design Patterns

Confidence-Gated Routing

• Route majority of inputs through cheap model
• Escalate only uncertain predictions to expensive model
• Trade-off: threshold controls accuracy vs latency
• Applicable beyond QEC: any two-stage inference pipeline

Hardware-Aware Co-Design

• Match decoder complexity to hardware throughput constraints
• Identify actual bottleneck (neural vs graph computation)
• Size batch for hardware saturation point

Validated vs Roadmap Contributions

• Clearly distinguish experimentally validated results from future directions
• Release complete benchmarking pipeline, models, and data
• Explicit scope boundaries in publications

Reusable Workflow

1. Define QEC code (surface code, distance d)
2. Generate training data (Stim simulator, circuit-level noise)
3. Train FFNN decoder (syndrome → correction)
4. Calibrate confidence threshold (accuracy-latency sweep)
5. Integrate MWPM fallback for low-confidence predictions
6. Benchmark across distances, noise models, batch sizes
7. Identify bottleneck → optimize accordingly

Pitfalls

• Confidence threshold sensitivity: Too high → excessive MWPM calls; too low → accuracy loss
• Neural saturation point: Beyond d=7, neural path is NOT the throughput bottleneck — optimize graph stage instead
• Noise model mismatch: Decoder trained on one noise model may degrade under different noise

References

• Paper: https://arxiv.org/abs/2607.05814
• Source code: https://github.com/Sumitchongder/adaptive-qec-decoder