NSN Integration Quick Start Guide
Get started with NSN integration in 5 minutes!
Installation
No additional dependencies required. The NSN integration uses existing quantum_integration packages.
Quick Examples
1. Backend-Aware Rank Selection (30 seconds)
from quantum_integration.nsn_integration import BackendAwareRankSelector, BackendType
selector = BackendAwareRankSelector()
recommendation = selector.get_rank_recommendation(
backend_type=BackendType.IBM_WASHINGTON,
compute_budget=1e8,
min_reliability=0.85
)
print(f"Recommended Rank: {recommendation['recommended_rank']}")
print(f"Rationale: {recommendation['rationale']}")
2. Multilingual Evaluation (1 minute)
from quantum_integration.nsn_integration import MultilingualNSNEvaluator
evaluator = MultilingualNSNEvaluator()
result = evaluator.evaluate_language_edit('indonesian', rank=64)
print(f"Accuracy: {result.edit_accuracy:.3f}")
print(f"Uncertainty: {result.uncertainty:.3f}")
3. Contributor Challenge (2 minutes)
from quantum_integration.nsn_integration import NSNLeaderboard
leaderboard = NSNLeaderboard()
challenge = leaderboard.create_challenge(
challenge_id="my_challenge",
title="My First Challenge",
description="Test multilingual editing",
languages=['english', 'chinese']
)
# Submit edit
rank_results = {
32: {'accuracy': 0.88, 'uncertainty': 0.12, 'flops': 1e7, 'efficiency': 0.009}
}
submission = leaderboard.submit_edit(
challenge_id="my_challenge",
contributor_id="me",
language="english",
edit_description="My edit",
rank_results=rank_results
)
rankings = leaderboard.get_leaderboard("my_challenge")
print(f"Position: {rankings[0]['position']}")
Run Complete Demo
python quantum_integration/nsn_integration/demo_complete_nsn_integration.py
Run Tests
python quantum_integration/nsn_integration/test_nsn_integration.py
Next Steps
- Read the full README.md for detailed documentation
- Explore visualization with NSNDashboard
- Integrate with LIMIT-Graph benchmarking
- Submit to contributor challenges
Support
Check the README.md or open an issue for help!