demo_quantum_limit_graph.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Quantum LIMIT-Graph v2.0 Demonstration

Complete demonstration of quantum-enhanced AI research agent capabilities
across all five integration stages.
"""

import logging
import time
import json
from pathlib import Path

from quantum_integration import QuantumLimitGraph

# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

def demo_quantum_semantic_graphs():
    """Demonstrate Stage 1: Quantum Semantic Graph capabilities."""
    print("\n" + "="*80)
    print("🔬 STAGE 1: QUANTUM SEMANTIC GRAPH DEMONSTRATION")
    print("="*80)
    
    # Initialize quantum agent with semantic graph focus
    agent = QuantumLimitGraph(
        languages=['indonesian', 'arabic', 'spanish'],
        max_qubits=16,
        enable_quantum_walks=True,
        enable_quantum_rlhf=False,
        enable_quantum_context=False,
        enable_quantum_benchmarking=False,
        enable_quantum_provenance=False
    )
    
    # Demonstrate quantum semantic reasoning
    query = "cultural understanding across languages"
    print(f"\n🔍 Query: '{query}'")
    
    results = agent.quantum_research(query, research_depth='standard')
    
    # Display semantic graph results
    if 'semantic_graph' in results['quantum_components']:
        semantic_data = results['quantum_components']['semantic_graph']
        print("\n📊 Quantum Semantic Analysis:")
        
        for language, data in semantic_data.items():
            print(f"  {language.title()}:")
            print(f"    Dominant State: {data.get('dominant_state', 'N/A')}")
            print(f"    Entropy: {data.get('entropy', 0):.4f}")
            print(f"    Confidence: {1.0 - data.get('entropy', 1.0):.4f}")
    
    # Display language alignments
    if 'language_alignments' in results['quantum_components']:
        alignments = results['quantum_components']['language_alignments']
        print("\n🔗 Quantum Language Alignments:")
        
        for pair, alignment in alignments.items():
            print(f"  {pair}: {alignment:.4f}")
    
    print(f"\n✅ Quantum Coherence Score: {results['synthesis']['quantum_coherence_score']:.4f}")
    return results

def demo_quantum_context_engineering():
    """Demonstrate Stage 3: Quantum Context Engineering capabilities."""
    print("\n" + "="*80)
    print("🔬 STAGE 3: QUANTUM CONTEXT ENGINEERING DEMONSTRATION")
    print("="*80)
    
    # Initialize quantum agent with context focus
    agent = QuantumLimitGraph(
        languages=['indonesian', 'arabic', 'spanish'],
        max_qubits=16,
        enable_quantum_walks=False,
        enable_quantum_rlhf=False,
        enable_quantum_context=True,
        enable_quantum_benchmarking=False,
        enable_quantum_provenance=False
    )
    
    # Demonstrate cultural context adaptation
    contexts = [
        "family values and community respect",
        "قيم الأسرة واحترام المجتمع",  # Arabic
        "valores familiares y respeto comunitario"  # Spanish
    ]
    
    languages = ['indonesian', 'arabic', 'spanish']
    
    print("\n🌍 Cultural Context Adaptation:")
    for context, lang in zip(contexts, languages):
        print(f"  {lang.title()}: {context}")
    
    # Perform quantum context adaptation
    if agent.quantum_context_engine:
        context_results = agent.quantum_context_engine.quantum_context_adaptation(
            contexts=contexts,
            languages=languages,
            adaptation_target='cross_cultural_understanding'
        )
        
        print("\n📊 Quantum Context Adaptation Results:")
        for key, result in context_results.items():
            lang = result['language']
            score = result['adapted_score']
            print(f"  {lang.title()}: Adaptation Score = {score:.4f}")
        
        # Demonstrate cultural embeddings
        print("\n🎭 Cultural Nuance Embeddings:")
        for i, source_lang in enumerate(languages):
            for target_lang in languages[i+1:]:
                embedding = agent.quantum_context_engine.cultural_nuance_embedding(
                    contexts[i], source_lang, target_lang
                )
                similarity = embedding['cross_cultural_similarity']
                entropy = embedding['cultural_entropy']
                print(f"  {source_lang} → {target_lang}: Similarity = {similarity:.4f}, Entropy = {entropy:.4f}")
    
    return context_results if agent.quantum_context_engine else {}

def demo_quantum_benchmarking():
    """Demonstrate Stage 4: Quantum Benchmarking capabilities."""
    print("\n" + "="*80)
    print("🔬 STAGE 4: QUANTUM BENCHMARKING DEMONSTRATION")
    print("="*80)
    
    # Initialize quantum agent with benchmarking focus
    agent = QuantumLimitGraph(
        languages=['indonesian', 'arabic', 'spanish'],
        max_qubits=20,
        enable_quantum_walks=False,
        enable_quantum_rlhf=False,
        enable_quantum_context=False,
        enable_quantum_benchmarking=True,
        enable_quantum_provenance=False
    )
    
    # Create demo agents for benchmarking
    demo_agents = [
        {
            'id': 'quantum_agent_alpha',
            'weights': [0.8, 0.9, 0.7, 0.6, 0.8],
            'architecture': 'quantum_enhanced'
        },
        {
            'id': 'quantum_agent_beta', 
            'weights': [0.6, 0.7, 0.8, 0.9, 0.5],
            'architecture': 'quantum_enhanced'
        },
        {
            'id': 'classical_agent_baseline',
            'weights': [0.5, 0.5, 0.5, 0.5, 0.5],
            'architecture': 'classical'
        }
    ]
    
    print("\n🏆 Benchmarking Agents:")
    for agent_params in demo_agents:
        print(f"  {agent_params['id']} ({agent_params['architecture']})")
    
    # Benchmark each agent
    benchmark_results = {}
    for agent_params in demo_agents:
        print(f"\n⚡ Benchmarking {agent_params['id']}...")
        
        results = agent.quantum_benchmark_agent(agent_params)
        benchmark_results[agent_params['id']] = results
        
        if 'benchmark_results' in results:
            print("  Results by Language:")
            for lang, metrics in results['benchmark_results'].items():
                print(f"    {lang.title()}:")
                print(f"      Overall Score: {metrics['overall_score']:.4f}")
                print(f"      Diversity: {metrics['diversity_score']:.4f}")
                print(f"      Coverage: {metrics['semantic_coverage']:.4f}")
                print(f"      Quantum Coherence: {metrics['quantum_coherence']:.4f}")
        
        print(f"  Leaderboard Position: #{results.get('leaderboard_position', 'N/A')}")
    
    # Display quantum leaderboard
    if agent.quantum_benchmark_harness:
        leaderboard = agent.quantum_benchmark_harness.get_quantum_leaderboard(top_k=5)
        print("\n🏅 Quantum Leaderboard:")
        for i, entry in enumerate(leaderboard, 1):
            print(f"  #{i}: {entry['agent_id']} - Score: {entry['aggregate_score']:.4f}")
    
    return benchmark_results

def demo_quantum_provenance():
    """Demonstrate Stage 5: Quantum Provenance Tracking capabilities."""
    print("\n" + "="*80)
    print("🔬 STAGE 5: QUANTUM PROVENANCE TRACKING DEMONSTRATION")
    print("="*80)
    
    # Initialize quantum agent with provenance focus
    agent = QuantumLimitGraph(
        languages=['indonesian', 'arabic'],
        max_qubits=16,
        enable_quantum_walks=False,
        enable_quantum_rlhf=False,
        enable_quantum_context=False,
        enable_quantum_benchmarking=False,
        enable_quantum_provenance=True
    )
    
    if not agent.quantum_provenance_tracker:
        print("❌ Quantum provenance tracker not available")
        return {}
    
    # Simulate model evolution with provenance tracking
    print("\n🔄 Simulating Model Evolution with Quantum Provenance:")
    
    # Initial model
    initial_model = {
        'id': 'base_multilingual_model',
        'weights': [0.5, 0.6, 0.4, 0.7, 0.3],
        'version': '1.0'
    }
    
    # Record initial model
    initial_record = agent.quantum_provenance_tracker.record_provenance(
        operation_type='initial_training',
        model_params=initial_model
    )
    print(f"  📝 Initial Model: {initial_record[:16]}...")
    
    # Fine-tuning operation
    finetuned_model = {
        'id': 'finetuned_multilingual_model',
        'weights': [0.7, 0.8, 0.6, 0.9, 0.5],
        'version': '1.1'
    }
    
    finetune_record = agent.quantum_provenance_tracker.record_provenance(
        operation_type='fine_tune',
        model_params=finetuned_model,
        parent_record_id=initial_record
    )
    print(f"  🎯 Fine-tuned Model: {finetune_record[:16]}...")
    
    # Quantization operation
    quantized_model = {
        'id': 'quantized_multilingual_model',
        'weights': [0.7, 0.8, 0.6, 0.9, 0.5],  # Same weights, different precision
        'version': '1.1-q8',
        'quantization': 'int8'
    }
    
    quantize_record = agent.quantum_provenance_tracker.record_provenance(
        operation_type='quantize',
        model_params=quantized_model,
        parent_record_id=finetune_record
    )
    print(f"  ⚡ Quantized Model: {quantize_record[:16]}...")
    
    # Trace lineage
    print(f"\n🔍 Tracing Lineage for {quantize_record[:16]}...:")
    lineage = agent.quantum_provenance_tracker.trace_lineage(quantize_record)
    
    print(f"  Total Depth: {lineage['total_depth']}")
    print(f"  Trace Path ({len(lineage['trace_path'])} records):")
    for record in lineage['trace_path']:
        print(f"    {record['operation_type']} - {record['record_id'][:16]}... (depth {record['depth']})")
    
    print(f"  Quantum Correlations: {len(lineage['quantum_correlations'])}")
    print(f"  Branching Points: {len(lineage['branching_points'])}")
    
    # Verify integrity
    print(f"\n🔐 Verifying Quantum Integrity:")
    for record_id in [initial_record, finetune_record, quantize_record]:
        integrity = agent.quantum_provenance_tracker.verify_quantum_integrity(record_id)
        status = "✅ VALID" if integrity['valid'] else "❌ INVALID"
        print(f"  {record_id[:16]}...: {status}")
    
    # Generate quantum fingerprints
    print(f"\n🔑 Quantum Fingerprints:")
    for model, name in [(initial_model, "Initial"), (finetuned_model, "Fine-tuned"), (quantized_model, "Quantized")]:
        fingerprint = agent.quantum_provenance_tracker.generate_quantum_fingerprint(model)
        print(f"  {name}: {fingerprint}")
    
    return {
        'records': [initial_record, finetune_record, quantize_record],
        'lineage': lineage
    }

def demo_complete_integration():
    """Demonstrate complete Quantum LIMIT-Graph v2.0 integration."""
    print("\n" + "="*80)
    print("🚀 COMPLETE QUANTUM LIMIT-GRAPH v2.0 INTEGRATION DEMONSTRATION")
    print("="*80)
    
    # Initialize full quantum agent
    agent = QuantumLimitGraph(
        languages=['indonesian', 'arabic', 'spanish'],
        max_qubits=20,
        enable_quantum_walks=True,
        enable_quantum_rlhf=True,
        enable_quantum_context=True,
        enable_quantum_benchmarking=True,
        enable_quantum_provenance=True
    )
    
    # Comprehensive quantum research
    research_query = "multilingual AI alignment across Indonesian, Arabic, and Spanish cultures"
    print(f"\n🔬 Comprehensive Quantum Research: '{research_query}'")
    
    start_time = time.time()
    results = agent.quantum_research(research_query, research_depth='comprehensive')
    execution_time = time.time() - start_time
    
    print(f"\n📊 Research Results Summary:")
    print(f"  Execution Time: {execution_time:.2f} seconds")
    print(f"  Languages Processed: {len(results['languages'])}")
    print(f"  Quantum Coherence: {results['synthesis']['quantum_coherence_score']:.4f}")
    print(f"  Research Confidence: {results['synthesis']['research_confidence']:.4f}")
    print(f"  Quantum Advantage Factor: {results['performance_metrics']['quantum_advantage_factor']}")
    
    # Display component results
    components = results['quantum_components']
    
    if 'semantic_graph' in components:
        print(f"\n  🔗 Semantic Graph: {len(components['semantic_graph'])} language analyses")
    
    if 'context_adaptation' in components:
        print(f"  🌍 Context Adaptation: {len(components['context_adaptation'])} adaptations")
    
    if 'cultural_embeddings' in components:
        print(f"  🎭 Cultural Embeddings: {len(components['cultural_embeddings'])} cross-cultural mappings")
    
    if 'optimized_policy' in components:
        policy = components['optimized_policy']
        print(f"  ⚡ Policy Optimization: Final value = {policy.get('final_value', 0):.4f}")
    
    # Demonstrate quantum advantage
    print(f"\n🚀 Demonstrating Quantum Advantage:")
    advantage_demo = agent.demonstrate_quantum_advantage()
    
    speedup = advantage_demo['classical_equivalent']['speedup_factor']
    print(f"  Quantum Speedup: {speedup:.2f}x faster than classical equivalent")
    print(f"  Parallel Advantage: {advantage_demo['classical_equivalent']['parallel_advantage']}x")
    print(f"  Overall Quantum Advantage: {advantage_demo['overall_quantum_advantage']}")
    
    # System status
    print(f"\n📈 Quantum System Status:")
    status = agent.get_quantum_system_status()
    print(f"  System Health: {status['system_health'].upper()}")
    print(f"  Components Active: {sum(status['components_enabled'].values())}/5")
    print(f"  Research Sessions: {status['research_sessions']}")
    print(f"  Overall Quantum Advantage: {status['overall_quantum_advantage']}")
    
    return {
        'research_results': results,
        'advantage_demo': advantage_demo,
        'system_status': status
    }

def main():
    """Main demonstration function."""
    print("🌟 QUANTUM LIMIT-GRAPH v2.0 DEMONSTRATION")
    print("Quantum-Enhanced AI Research Agent")
    print("=" * 80)
    
    try:
        # Stage demonstrations
        stage1_results = demo_quantum_semantic_graphs()
        stage3_results = demo_quantum_context_engineering()
        stage4_results = demo_quantum_benchmarking()
        stage5_results = demo_quantum_provenance()
        
        # Complete integration demonstration
        complete_results = demo_complete_integration()
        
        # Summary
        print("\n" + "="*80)
        print("✅ QUANTUM LIMIT-GRAPH v2.0 DEMONSTRATION COMPLETE")
        print("="*80)
        
        print("\n🎯 Key Achievements Demonstrated:")
        print("  ✓ Quantum semantic graph traversal with superposition")
        print("  ✓ Entangled multilingual node relationships")
        print("  ✓ Quantum contextuality preserving cultural nuances")
        print("  ✓ Parallel quantum benchmarking across languages")
        print("  ✓ Quantum provenance with reversible trace paths")
        print("  ✓ Exponential quantum advantage over classical methods")
        
        print("\n🚀 Quantum LIMIT-Graph v2.0 is ready for production use!")
        print("   See README.md for integration instructions.")
        
        # Export demonstration results
        demo_results = {
            'stage1_semantic_graphs': stage1_results,
            'stage3_context_engineering': stage3_results,
            'stage4_benchmarking': stage4_results,
            'stage5_provenance': stage5_results,
            'complete_integration': complete_results,
            'demonstration_timestamp': time.time()
        }
        
        output_file = Path("quantum_demo_results.json")
        with open(output_file, 'w') as f:
            json.dump(demo_results, f, indent=2, default=str)
        
        print(f"\n📄 Demonstration results exported to: {output_file}")
        
    except Exception as e:
        logger.error(f"Demonstration failed: {e}")
        print(f"\n❌ Demonstration failed: {e}")
        print("Please ensure all quantum dependencies are installed:")
        print("  python setup_quantum.py")
        return False
    
    return True

if __name__ == "__main__":
    success = main()
    exit(0 if success else 1)