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:

```python
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:

```python
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:

```python
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:

```python
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

```bash
# 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

```bash
# 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

```json
{
  "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](https://huggingface.co/spaces/your-org/nsn-integration-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](README.md)
- **API Reference**: [V2.4.0_SCENARIOS_SUMMARY.md](V2.4.0_SCENARIOS_SUMMARY.md)
- **Quick Start**: [QUICK_START_V2.4.0.md](QUICK_START_V2.4.0.md)
- **Demo Scripts**: [demo_v2.4.0_scenarios.py](demo_v2.4.0_scenarios.py)
- **Test Suite**: [test_v2.4.0_scenarios.py](test_v2.4.0_scenarios.py)

---

## 💬 Community

- **Discord**: [Join our server](https://discord.gg/quantum-limit-graph)
- **GitHub Discussions**: [Ask questions](https://github.com/your-repo/quantum-limit-graph/discussions)
- **Twitter**: [@QuantumLIMIT](https://twitter.com/QuantumLIMIT)

---

## 📝 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

---

## 🤝 Support

Need help? Reach out:
- Open an issue on GitHub
- Ask in Discord #nsn-integration channel
- Email: support@quantum-limit-graph.org

---

**Happy Contributing! 🚀**

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