wifi-densepose/vendor/ruvector/crates/ruQu/docs/RESEARCH_DISCOVERIES.md

Research Discoveries for ruQu Enhancement

Compiled: January 2026

This document captures state-of-the-art research findings that can inform further improvements to ruQu's coherence gate architecture.

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1. Real-Time Decoding at Scale

DECONET System (April 2025)

Source: arXiv:2504.11805

DECONET is a first-of-its-kind decoding system that scales to thousands of logical qubits with lattice surgery support. Key innovations:

• Network-integrated hybrid tree-grid structure: O(log(l)) latency increase as system grows
• Resource scaling: O(l × log(l)) compute, O(l) I/O for l logical qubits
• Union-Find decoder: 100× higher accuracy than greedy algorithms
• Prototype: 100 logical qubits on 5 VMK-180 FPGAs

Relevance to ruQu: Our ParallelFabric uses flat parallelism. Consider hierarchical tree-grid topology for 1000+ tile scaling.

Google Below-Threshold (2025)

Source: Nature 2024

Google achieved Λ = 2.14 ± 0.02 error suppression when increasing code distance by 2, with a 101-qubit distance-7 code achieving 0.143% error per cycle.

Relevance to ruQu: Our three-filter decision pipeline should target similar sub-0.2% false positive rates.

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2. Hardware-Accelerated Decoding

Riverlane Collision Clustering Decoder

Source: Riverlane Blog

Platform	Qubits	Latency	Power
FPGA	881	810 ns	-
ASIC	1,057	240 ns	8 mW

The ASIC fits in 0.06 mm² - suitable for cryogenic deployment.

Relevance to ruQu: Our coherence simulation achieves 468ns P99. ASIC compilation of the hot path could reach 240ns.

QASBA: Sparse Blossom on FPGA

Source: ACM TRETS

• 25× performance vs software baseline
• 304× energy efficiency improvement

Relevance to ruQu: Our min-cut computation is the hot path. FPGA synthesis of SubpolynomialMinCut could yield similar gains.

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3. Adaptive Syndrome Extraction

PRX Quantum (July 2025)

Source: PRX Quantum

Adaptive syndrome extraction measures only stabilizers likely to provide useful information:

• 10× lower logical error rates vs non-adaptive
• Fewer CNOT gates and physical qubits
• Uses 4,2,2 concatenated with hypergraph product code

Relevance to ruQu: This validates our coherence gate philosophy - don't process everything, focus on what matters. Consider:

• Tracking which detectors fire frequently (already in stim.rs)
• Skip syndrome processing for "quiet" regions
• Adaptive measurement scheduling

Multi-Agent RL for QEC

Source: arXiv:2509.03974

Uses reinforcement learning bandits to:

• Evaluate fidelity after recovery
• Determine when retraining is necessary
• Optimize encoder, syndrome measurement, and recovery jointly

Relevance to ruQu: Our AdaptiveThresholds uses EMA-based learning. Consider upgrading to bandit-based exploration for threshold optimization.

Window-Based Drift Estimation (Nov 2025)

Source: arXiv:2511.09491

Estimates noise drift profiles from syndrome data alone, then adapts decoder parameters.

Relevance to ruQu: Integrate drift detection into adaptive.rs:

pub fn detect_drift(&mut self, window: &[SyndromeStats]) -> Option<DriftProfile> {
    // Detect if noise characteristics are shifting
    // Adjust thresholds proactively
}

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4. Mixture-of-Depths for Efficiency

MoD (DeepMind, 2024)

Source: arXiv:2404.02258

• 50% FLOPs reduction while matching dense transformer performance
• Per-token dynamic routing (skip middle layers for "resolved" tokens)
• Different from early-exit: tokens can skip middle layers then attend

Status: Already implemented in attention.rs via MincutDepthRouter integration.

Mixture-of-Recursions (NeurIPS 2025)

Source: arXiv:2507.10524

Combines parameter sharing + adaptive computation:

• Reuses shared layer stack across recursion steps
• Lightweight routers assign recursion depth per-token
• Token-level early exiting for simple predictions

Relevance to ruQu: Consider recursive tile processing:

pub fn process_recursive(&mut self, syndrome: &SyndromeDelta, max_depth: usize) -> GateDecision {
    for depth in 0..max_depth {
        let decision = self.process_at_depth(syndrome, depth);
        if decision.confidence > EARLY_EXIT_THRESHOLD {
            return decision;  // Exit early for clear cases
        }
    }
    decision
}

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5. Fusion Blossom Performance

Fusion Blossom Decoder

Source: arXiv:2305.08307, GitHub

• 1 million measurement rounds/second at d=33
• 0.7 ms latency in stream mode at d=21
• 58 ns per non-trivial measurement on 64-core machine
• O(N) complexity for defect vertices N

Status: Already integrated via decoder.rs feature. Consider:

• Enabling parallel fusion mode in production
• Streaming mode for real-time applications

PyMatching V2 Comparison

PyMatching V2 achieves 5-20× single-thread speedup over Fusion Blossom. The algorithms are compatible - combining them could yield another 5-20× improvement.

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6. Graph Neural Networks for QEC

QSeer (May 2025)

Source: arXiv:2505.06810

GNN for QAOA parameter prediction:

• 6-68% improvement in approximation ratio
• 5-10× convergence speedup
• Supports variable-depth circuits and weighted Max-Cut

Relevance to ruQu: Train a small GNN to predict optimal thresholds from syndrome graph structure:

pub struct ThresholdPredictor {
    model: OnnxModel,  // Export trained model
}

impl ThresholdPredictor {
    pub fn predict(&self, graph_embedding: &[f32]) -> GateThresholds {
        // Use learned model for threshold prediction
    }
}

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Implementation Priority Matrix

Enhancement	Impact	Effort	Priority
Hierarchical tree-grid topology	High	High	P2
Drift detection in adaptive.rs	High	Medium	P1
Recursive early-exit processing	Medium	Low	P1
Bandit-based threshold exploration	Medium	Medium	P2
FPGA synthesis of min-cut	Very High	Very High	P3
GNN threshold predictor	Medium	High	P3
Streaming Fusion mode	High	Low	P1

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Immediate Next Steps

1. Drift Detection: Add window-based drift estimation to adaptive.rs
2. Early-Exit Depth: Implement confidence-based early exit in tile processing
3. Streaming Decoder: Enable Fusion Blossom streaming mode for <1ms latency
4. Parallel Fusion: Configure parallel fusion on 64+ core systems

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References

1. DECONET: arxiv.org/abs/2504.11805
2. Google Below-Threshold: nature.com/articles/s41586-024-08449-y
3. Riverlane CC Decoder: riverlane.com
4. Adaptive Syndrome Extraction: doi.org/10.1103/ps3r-wf84
5. Multi-Agent RL QEC: arxiv.org/pdf/2509.03974
6. Drift Estimation: arxiv.org/html/2511.09491
7. Mixture-of-Depths: arxiv.org/html/2404.02258v1
8. Mixture-of-Recursions: arxiv.org/html/2507.10524v1
9. Fusion Blossom: arxiv.org/abs/2305.08307
10. QSeer GNN: arxiv.org/abs/2505.06810
11. QASBA FPGA: dl.acm.org/doi/10.1145/3723168