CONTRIBUTOR_GUIDE.md

NSN Integration Contributor Guide

Welcome to the Quantum LIMIT-Graph v2.4.0 NSN Integration contributor challenges! This guide will help you participate in our four main challenge scenarios.

🎯 Challenge Scenarios

Scenario 1: Real-Time Backend-Aware Rank Adaptation

Module: backend_telemetry_rank_adapter.py

Function: Adjust NSN rank based on backend health (error rate, coherence time, gate fidelity)

Your Task: Submit edits optimized for dynamic rank shifts

Leaderboard Metric: Responsiveness vs reliability trade-off

Dashboard Panel: Line chart of rank vs reliability across backend states

How to Contribute:

from quantum_integration.nsn_integration import BackendTelemetryRankAdapter

# Initialize adapter
adapter = BackendTelemetryRankAdapter()

# Submit your telemetry-aware edit
result = adapter.adapt_rank(
    backend_id='your_contributor_id_backend',
    telemetry={
        'error_rate': 0.025,  # Your measured error rate
        'coherence_time': 110.0,  # Your coherence time (μs)
        'gate_fidelity': 0.97  # Your gate fidelity
    },
    current_rank=128
)

# Export for leaderboard
adapter.export_telemetry_edits('my_submission.json')

Scoring:

• Responsiveness: How quickly your adaptation occurs (higher is better)
• Reliability: Predicted reliability at adapted rank (0-1, higher is better)
• Final Score: 0.6 * reliability + 0.4 * (responsiveness / 1000)

Tips:

• Test across multiple backend states (optimal, degraded, poor)
• Optimize for both speed and accuracy
• Consider calibration age in your strategy

---

Scenario 2: Cross-Lingual Edit Propagation

Module: edit_propagation_engine.py

Function: Transfer edits from high-resource to low-resource languages using containment scores

Your Task: Submit propagation strategies and containment visualizations

Leaderboard Metric: Quality score of propagated edits

Dashboard Panel: Heatmap of containment scores + flow arrows

How to Contribute:

from quantum_integration.nsn_integration import EditPropagationEngine
import numpy as np

# Initialize engine
engine = EditPropagationEngine()

# Create your edit vector
edit_vector = np.random.randn(256) * 0.1  # Your edit

# Propagate from high-resource to low-resource language
result = engine.propagate_edit(
    source_lang='english',
    target_lang='indonesian',
    rank=128,
    edit_vector=edit_vector
)

print(f"Quality Score: {result.quality_score:.3f}")
print(f"Containment: {result.containment_score:.3f}")

Scoring:

• Quality Score: Predicted quality of propagated edit (0-1)
• Containment Score: Subspace containment (0-1)
• Final Score: 0.7 * quality_score + 0.3 * containment_score

Tips:

• Focus on high-containment language pairs (>0.75)
• Test multi-hop propagation paths
• Visualize containment heatmaps to find optimal paths

Bonus Points:

• Submit novel propagation strategies
• Discover new high-containment language pairs
• Create visualization tools

---

Scenario 3: Contributor-Aware Rank Feedback

Module: rank_feedback_generator.py

Function: Recommend optimal ranks based on contributor history

Your Task: Submit edits across ranks and analyze feedback

Leaderboard Metric: Efficiency badge (accuracy/FLOPs)

Dashboard Panel: Personalized rank suggestions + unexplored rank-language pairs

How to Contribute:

from quantum_integration.nsn_integration import RankFeedbackGenerator

# Initialize generator
generator = RankFeedbackGenerator()

# Submit multiple edits across different ranks
submissions = [
    {'language': 'english', 'rank': 32, 'accuracy': 0.88, 'flops': 1.02e7, 'uncertainty': 0.12},
    {'language': 'english', 'rank': 64, 'accuracy': 0.92, 'flops': 4.1e7, 'uncertainty': 0.08},
    {'language': 'chinese', 'rank': 64, 'accuracy': 0.90, 'flops': 4.1e7, 'uncertainty': 0.09}
]

for sub in submissions:
    generator.record_submission(
        contributor_id='your_id',
        language=sub['language'],
        rank=sub['rank'],
        accuracy=sub['accuracy'],
        flops=sub['flops'],
        uncertainty=sub['uncertainty']
    )

# Get personalized recommendation
recommendation = generator.recommend_rank('your_id')
print(f"Badge: {recommendation.personalized_badge}")
print(f"Recommended Rank: {recommendation.recommended_rank}")

# Get feedback panel
panel = generator.generate_feedback_panel('your_id')
print(f"Suggestions: {panel['suggestions']}")

Scoring:

• Efficiency: accuracy / flops (higher is better)
• Diversity: Number of unique rank-language pairs tested
• Final Score: 0.6 * avg_efficiency * 1e8 + 0.4 * diversity_bonus

Badge System:

• 🏆 Master Contributor: 50+ submissions, 10+ languages
• ⚡ Efficiency Expert: Efficiency > 1e-7
• 🎯 Accuracy Champion: Avg accuracy > 0.95
• 🔬 Rank Explorer: Tested 5+ ranks
• 🌍 Multilingual Specialist: 8+ languages
• 💪 Active Contributor: 20+ submissions
• 📈 Rising Star: 10+ submissions
• 🚀 Getting Started: First submissions

Tips:

• Test across multiple ranks to find your optimal range
• Focus on unexplored rank-language pairs for bonus points
• Balance accuracy and efficiency

---

Scenario 4: Ensemble Inference Across Backends

Module: ensemble_inference_manager.py

Function: Run edits across IBM Manila, Washington, and Russian simulators

Your Task: Submit ensemble edits and analyze backend agreement

Leaderboard Metric: Agreement score + reliability boost

Dashboard Panel: Agreement matrix + backend consensus heatmap

How to Contribute:

from quantum_integration.nsn_integration import EnsembleInferenceManager
import numpy as np

# Initialize manager
manager = EnsembleInferenceManager()

# Create your edit
edit_vector = np.random.randn(256) * 0.1

# Run ensemble inference
result = manager.run_ensemble_inference(
    edit_vector=edit_vector,
    backend_list=['ibm_manila', 'ibm_washington', 'russian_simulator']
)

print(f"Agreement Score: {result.agreement_score:.3f}")
print(f"Reliability Boost: {result.reliability_boost:.3f}")
print(f"Best Backend: {result.best_backend}")

Scoring:

• Agreement Score: Pairwise agreement across backends (0-1)
• Reliability Boost: Improvement from ensemble consensus (0-1)
• Final Score: 0.5 * agreement_score + 0.5 * reliability_boost

Tips:

• Test with 3+ backends for maximum reliability boost
• Analyze agreement matrices to understand backend behavior
• Submit edits that achieve high consensus

Bonus Points:

• Discover backend-specific optimization strategies
• Submit edits with >0.95 agreement across all backends
• Create ensemble strategies for specific use cases

---

🚀 Getting Started

Installation

# Clone repository
git clone https://github.com/your-repo/quantum-limit-graph.git
cd quantum-limit-graph

# Install dependencies
pip install -r quantum_integration/nsn_integration/requirements_dashboard.txt

# Run tests
pytest quantum_integration/nsn_integration/test_v2.4.0_scenarios.py -v

Running the Dashboard Locally

# Launch Gradio dashboard
python quantum_integration/nsn_integration/huggingface_dashboard.py

# Open browser to http://localhost:7860

Submitting Your Contributions

1. Fork the repository
2. Create your submission branch: git checkout -b my-nsn-submission
3. Run your experiments and save results
4. Export your data: Use the export functions in each module
5. Create a submission file: submissions/your_id_YYYYMMDD.json
6. Submit a pull request with your results

Submission Format

{
  "contributor_id": "your_github_username",
  "timestamp": "2025-01-15T10:30:00Z",
  "scenarios": {
    "telemetry_adaptation": {
      "submissions": [...],
      "avg_responsiveness": 1250.5,
      "avg_reliability": 0.92
    },
    "edit_propagation": {
      "submissions": [...],
      "avg_quality": 0.85,
      "avg_containment": 0.78
    },
    "rank_feedback": {
      "submissions": [...],
      "efficiency": 8.5e-8,
      "badge": "⚡ Efficiency Expert"
    },
    "ensemble_inference": {
      "submissions": [...],
      "avg_agreement": 0.89,
      "avg_reliability_boost": 0.82
    }
  }
}

---

📊 Leaderboard

View the live leaderboard at: Hugging Face Spaces Dashboard

Current Top Contributors

Rank	Contributor	Total Score	Badge	Submissions
1	contributor_001	95.2	🏆 Master	52
2	contributor_002	89.7	⚡ Efficiency	38
3	contributor_003	85.3	🎯 Accuracy	45

---

🎁 Rewards & Recognition

Monthly Prizes

• 🥇 1st Place: Featured in research paper + $500 prize
• 🥈 2nd Place: GitHub sponsor badge + $300 prize
• 🥉 3rd Place: Contributor spotlight + $200 prize

Special Awards

• 🌟 Innovation Award: Most creative propagation strategy
• 🔬 Research Award: Best analysis and visualization
• 🌍 Impact Award: Highest quality low-resource language edits

---

📚 Resources

• Documentation: README.md
• API Reference: V2.4.0_SCENARIOS_SUMMARY.md
• Quick Start: QUICK_START_V2.4.0.md
• Demo Scripts: demo_v2.4.0_scenarios.py
• Test Suite: test_v2.4.0_scenarios.py

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💬 Community

• Discord: Join our server
• GitHub Discussions: Ask questions
• Twitter: @QuantumLIMIT

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📝 Code of Conduct

• Be respectful and collaborative
• Share knowledge and help others
• Follow scientific integrity guidelines
• Cite sources and give credit
• Report issues and bugs constructively

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🤝 Support

Need help? Reach out:

• Open an issue on GitHub
• Ask in Discord #nsn-integration channel
• Email: support@quantum-limit-graph.org

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Happy Contributing! 🚀

Let's push the boundaries of quantum-enhanced multilingual model editing together!