Tutorials

Step-by-step tutorials for mastering QuantumLangChain development.

Table of Contents

1. Tutorial 1: Your First Quantum Chain
2. Tutorial 2: Building Quantum Memory Systems
3. Tutorial 3: Multi-Agent Quantum Collaboration
4. Tutorial 4: Advanced Vector Search with Quantum Enhancement
5. Tutorial 5: Quantum Tool Integration
6. Tutorial 6: Building a Quantum RAG System
7. Tutorial 7: Production Deployment

Tutorial 1: Your First Quantum Chain

Overview

In this tutorial, you'll create your first quantum-enhanced reasoning chain that leverages quantum superposition for parallel processing.

Learning Objectives

• Set up QuantumLangChain environment
• Create and configure a quantum backend
• Build a basic quantum chain
• Execute quantum-enhanced queries
• Monitor quantum coherence

Prerequisites

• Python 3.9+ installed
• Basic understanding of quantum computing concepts
• Familiarity with async/await in Python

Step 1: Installation and Setup

First, let's install QuantumLangChain and set up our environment:

# Create a new virtual environment
python -m venv quantum-tutorial
source quantum-tutorial/bin/activate  # Linux/Mac
# or
quantum-tutorial\Scripts\activate  # Windows

# Install QuantumLangChain with quantum backends
pip install quantumlangchain[qiskit]

# Install additional dependencies for this tutorial
pip install jupyter matplotlib

Step 2: Import Dependencies

Create a new file called tutorial_1.py and start with the imports:

import asyncio
import time
from quantumlangchain import QLChain, QuantumMemory, QuantumConfig
from quantumlangchain.backends import QiskitBackend

# For visualization
import matplotlib.pyplot as plt

Step 3: Configure Quantum Backend

Set up your quantum computing backend. We'll start with a simulator:

async def setup_quantum_environment():
    """Set up quantum computing environment."""

    # Create quantum configuration
    config = QuantumConfig(
        num_qubits=4,           # Start with 4 qubits
        circuit_depth=6,        # Moderate circuit depth
        decoherence_threshold=0.2,  # Relaxed for learning
        backend_type="qiskit",
        shots=1024,             # Number of measurements
        optimization_level=1    # Basic optimization
    )

    # Initialize quantum backend
    backend = QiskitBackend(config=config)
    await backend.initialize()

    print(f"Quantum backend initialized: {backend.get_backend_info()}")
    return backend

Step 4: Create Quantum Memory

Now let's set up quantum memory for our chain:

async def setup_quantum_memory(backend):
    """Set up quantum memory system."""

    # Create quantum memory with both classical and quantum dimensions
    memory = QuantumMemory(
        classical_dim=384,      # Classical embedding dimension
        quantum_dim=4,          # Quantum register size
        backend=backend
    )

    # Initialize the memory system
    await memory.initialize()

    print("Quantum memory initialized")
    return memory

Step 5: Build Your First Quantum Chain

Create the quantum chain that will process our queries:

async def create_quantum_chain(memory, backend):
    """Create quantum reasoning chain."""

    # Configure the quantum chain
    chain_config = {
        'enable_superposition': True,      # Enable quantum superposition
        'max_parallel_branches': 3,        # Process up to 3 reasoning paths
        'coherence_threshold': 0.5,        # Minimum coherence required
        'reasoning_depth': 3,              # Number of reasoning steps
        'enable_entanglement': True        # Enable component entanglement
    }

    # Create the quantum chain
    chain = QLChain(
        memory=memory,
        backend=backend,
        config=chain_config
    )

    # Initialize the chain
    await chain.initialize()

    print("Quantum chain created successfully")
    return chain

Step 6: Execute Your First Quantum Query

Now let's test our quantum chain with some queries:

async def execute_quantum_queries(chain):
    """Execute sample queries to test quantum chain."""

    # Define test queries
    queries = [
        "What are the key principles of quantum computing?",
        "How does quantum superposition differ from classical bits?",
        "Explain quantum entanglement in simple terms."
    ]

    results = []

    for i, query in enumerate(queries, 1):
        print(f"\n--- Query {i}: {query} ---")

        # Record start time
        start_time = time.time()

        # Execute query with quantum enhancement
        result = await chain.arun(
            input_text=query,
            quantum_enhanced=True,
            enable_monitoring=True
        )

        # Record execution time
        execution_time = time.time() - start_time

        # Display results
        print(f"Answer: {result['output']}")
        print(f"Quantum coherence: {result['quantum_coherence']:.3f}")
        print(f"Parallel branches: {result.get('execution_branches', 'N/A')}")
        print(f"Execution time: {execution_time:.2f} seconds")

        # Store results for analysis
        results.append({
            'query': query,
            'result': result,
            'execution_time': execution_time
        })

    return results

Step 7: Monitor Quantum Performance

Let's create a function to analyze the quantum performance:

async def analyze_quantum_performance(chain, results):
    """Analyze quantum chain performance."""

    print("\n=== Quantum Performance Analysis ===")

    # Get chain statistics
    stats = await chain.get_execution_stats()

    print(f"Total executions: {stats.get('total_executions', 0)}")
    print(f"Average coherence: {stats.get('avg_coherence', 0):.3f}")
    print(f"Success rate: {stats.get('success_rate', 0):.2%}")
    print(f"Average execution time: {stats.get('avg_execution_time', 0):.2f}s")

    # Analyze individual results
    coherence_levels = [r['result']['quantum_coherence'] for r in results]
    execution_times = [r['execution_time'] for r in results]

    print(f"\nCoherence range: {min(coherence_levels):.3f} - {max(coherence_levels):.3f}")
    print(f"Execution time range: {min(execution_times):.2f}s - {max(execution_times):.2f}s")

    return stats

Step 8: Visualize Results

Create visualizations to understand quantum performance:

def visualize_results(results):
    """Create visualizations of quantum execution results."""

    # Extract data for plotting
    coherence_levels = [r['result']['quantum_coherence'] for r in results]
    execution_times = [r['execution_time'] for r in results]
    query_labels = [f"Query {i+1}" for i in range(len(results))]

    # Create subplots
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))

    # Plot quantum coherence
    ax1.bar(query_labels, coherence_levels, color='blue', alpha=0.7)
    ax1.set_title('Quantum Coherence by Query')
    ax1.set_ylabel('Coherence Level')
    ax1.set_ylim(0, 1)
    ax1.grid(True, alpha=0.3)

    # Plot execution times
    ax2.bar(query_labels, execution_times, color='green', alpha=0.7)
    ax2.set_title('Execution Time by Query')
    ax2.set_ylabel('Time (seconds)')
    ax2.grid(True, alpha=0.3)

    plt.tight_layout()
    plt.savefig('quantum_chain_performance.png', dpi=300, bbox_inches='tight')
    plt.show()

    print("Performance visualization saved as 'quantum_chain_performance.png'")

Step 9: Main Execution Function

Put it all together in a main function:

async def main():
    """Main tutorial execution function."""

    print("=== QuantumLangChain Tutorial 1: Your First Quantum Chain ===\n")

    try:
        # Step 1: Set up quantum environment
        print("Step 1: Setting up quantum environment...")
        backend = await setup_quantum_environment()

        # Step 2: Create quantum memory
        print("\nStep 2: Setting up quantum memory...")
        memory = await setup_quantum_memory(backend)

        # Step 3: Create quantum chain
        print("\nStep 3: Creating quantum chain...")
        chain = await create_quantum_chain(memory, backend)

        # Step 4: Execute quantum queries
        print("\nStep 4: Executing quantum queries...")
        results = await execute_quantum_queries(chain)

        # Step 5: Analyze performance
        print("\nStep 5: Analyzing quantum performance...")
        stats = await analyze_quantum_performance(chain, results)

        # Step 6: Create visualizations
        print("\nStep 6: Creating visualizations...")
        visualize_results(results)

        print("\n=== Tutorial 1 Complete! ===")
        print("You've successfully created and executed your first quantum chain!")

    except Exception as e:
        print(f"Tutorial error: {e}")
        import traceback
        traceback.print_exc()

# Run the tutorial
if __name__ == "__main__":
    asyncio.run(main())

Step 10: Run and Experiment

Save your tutorial_1.py file and run it:

python tutorial_1.py

Expected Output

You should see output similar to:

=== QuantumLangChain Tutorial 1: Your First Quantum Chain ===

Step 1: Setting up quantum environment...
Quantum backend initialized: {'backend_type': 'qiskit', 'num_qubits': 4, 'shots': 1024}

Step 2: Setting up quantum memory...
Quantum memory initialized

Step 3: Creating quantum chain...
Quantum chain created successfully

Step 4: Executing quantum queries...

--- Query 1: What are the key principles of quantum computing? ---
Answer: The key principles of quantum computing include superposition...
Quantum coherence: 0.867
Parallel branches: 3
Execution time: 1.23 seconds

...

=== Quantum Performance Analysis ===
Total executions: 3
Average coherence: 0.834
Success rate: 100.00%
Average execution time: 1.45s

Tutorial 1 Complete!

Key Concepts Learned

1. Quantum Configuration: How to set up quantum parameters
2. Quantum Memory: Creating memory systems with quantum enhancement
3. Quantum Chains: Building reasoning chains with superposition
4. Coherence Monitoring: Tracking quantum state quality
5. Performance Analysis: Understanding quantum execution metrics

Next Steps

• Experiment with different quantum configurations
• Try adjusting the number of qubits and circuit depth
• Monitor how coherence affects performance
• Explore the relationship between superposition and reasoning quality

---

Tutorial 2: Building Quantum Memory Systems

Overview

Learn to build sophisticated quantum memory systems with entanglement, reversibility, and quantum search capabilities.

Learning Objectives

• Implement quantum-enhanced memory storage
• Create memory entanglements
• Use reversible memory snapshots
• Perform quantum similarity search
• Optimize memory performance

Step 1: Advanced Memory Configuration

import asyncio
import numpy as np
from quantumlangchain import QuantumMemory, QuantumConfig
from quantumlangchain.backends import QiskitBackend

async def create_advanced_memory():
    """Create advanced quantum memory system."""

    # Enhanced quantum configuration
    config = QuantumConfig(
        num_qubits=6,           # More qubits for complex operations
        circuit_depth=8,        # Deeper circuits for better entanglement
        decoherence_threshold=0.1,  # Stricter coherence requirements
        backend_type="qiskit",
        shots=2048,
        optimization_level=2
    )

    # Initialize backend
    backend = QiskitBackend(config=config)
    await backend.initialize()

    # Create quantum memory with larger dimensions
    memory = QuantumMemory(
        classical_dim=768,      # Larger embedding dimension
        quantum_dim=6,          # Match qubit count
        backend=backend,
        config={
            'enable_compression': True,     # Enable memory compression
            'auto_snapshot': True,          # Automatic snapshots
            'snapshot_interval': 300,       # 5-minute intervals
            'max_entanglements': 10,        # Limit entanglements
            'coherence_monitoring': True    # Monitor coherence
        }
    )

    await memory.initialize()
    print("Advanced quantum memory system initialized")
    return memory

Step 2: Structured Knowledge Storage

async def build_knowledge_base(memory):
    """Build a structured knowledge base in quantum memory."""

    # Define knowledge categories
    knowledge_base = {
        'quantum_computing': [
            "Quantum computers use qubits that can exist in superposition states",
            "Quantum entanglement allows qubits to be correlated across distances",
            "Quantum gates manipulate qubit states to perform computations",
            "Quantum algorithms can provide exponential speedups for certain problems"
        ],
        'machine_learning': [
            "Neural networks learn patterns from data through training",
            "Deep learning uses multiple layers to extract hierarchical features",
            "Reinforcement learning optimizes actions through trial and error",
            "Transfer learning applies knowledge from one domain to another"
        ],
        'quantum_ml': [
            "Quantum machine learning combines quantum computing with ML algorithms",
            "Variational quantum circuits can be used as quantum neural networks",
            "Quantum feature maps encode classical data in quantum states",
            "Quantum advantage in ML may come from exponential state spaces"
        ]
    }

    # Store knowledge with category metadata
    stored_keys = {}

    for category, facts in knowledge_base.items():
        category_keys = []

        for i, fact in enumerate(facts):
            key = f"{category}_{i+1}"

            # Store with quantum enhancement and metadata
            await memory.store(
                key=key,
                value=fact,
                quantum_enhanced=True,
                metadata={
                    'category': category,
                    'fact_id': i+1,
                    'importance': np.random.uniform(0.7, 1.0),
                    'timestamp': asyncio.get_event_loop().time()
                }
            )

            category_keys.append(key)
            print(f"Stored: {key}")

        stored_keys[category] = category_keys

    return stored_keys

Step 3: Create Knowledge Entanglements

async def create_knowledge_entanglements(memory, stored_keys):
    """Create strategic entanglements between related knowledge."""

    entanglements = {}

    # Entangle quantum computing facts
    qc_entanglement = await memory.entangle_memories(
        keys=stored_keys['quantum_computing'],
        entanglement_strength=0.9,
        purpose="quantum_computing_coherence"
    )
    entanglements['quantum_computing'] = qc_entanglement
    print(f"Created quantum computing entanglement: {qc_entanglement}")

    # Entangle machine learning facts
    ml_entanglement = await memory.entangle_memories(
        keys=stored_keys['machine_learning'],
        entanglement_strength=0.8,
        purpose="ml_knowledge_coherence"
    )
    entanglements['machine_learning'] = ml_entanglement
    print(f"Created ML entanglement: {ml_entanglement}")

    # Cross-domain entanglement (quantum ML)
    cross_domain_keys = (
        stored_keys['quantum_computing'][:2] +  # First 2 QC facts
        stored_keys['machine_learning'][:2] +   # First 2 ML facts
        stored_keys['quantum_ml']              # All quantum ML facts
    )

    cross_entanglement = await memory.entangle_memories(
        keys=cross_domain_keys,
        entanglement_strength=0.7,
        purpose="quantum_ml_bridge"
    )
    entanglements['cross_domain'] = cross_entanglement
    print(f"Created cross-domain entanglement: {cross_entanglement}")

    return entanglements

Step 4: Quantum Search and Retrieval

async def demonstrate_quantum_search(memory):
    """Demonstrate quantum-enhanced search capabilities."""

    print("\n=== Quantum Search Demonstrations ===")

    # Test queries for different search scenarios
    test_queries = [
        {
            'query': "How do quantum computers process information?",
            'expected_category': 'quantum_computing',
            'search_type': 'semantic'
        },
        {
            'query': "What is deep learning and neural networks?",
            'expected_category': 'machine_learning', 
            'search_type': 'semantic'
        },
        {
            'query': "quantum algorithms machine learning exponential",
            'expected_category': 'quantum_ml',
            'search_type': 'keyword'
        }
    ]

    search_results = []

    for i, test in enumerate(test_queries, 1):
        print(f"\n--- Search Test {i} ---")
        print(f"Query: {test['query']}")
        print(f"Search Type: {test['search_type']}")

        # Perform quantum similarity search
        results = await memory.similarity_search(
            query=test['query'],
            top_k=3,
            quantum_enhanced=True,
            search_algorithm='amplitude_amplification'
        )

        print(f"Found {len(results)} results:")
        for j, result in enumerate(results):
            print(f"  {j+1}. Score: {result['similarity']:.3f}")
            print(f"     Category: {result['metadata']['category']}")
            print(f"     Content: {result['content'][:60]}...")
            print(f"     Quantum coherence: {result.get('quantum_coherence', 'N/A')}")

        search_results.append({
            'test': test,
            'results': results,
            'top_score': results[0]['similarity'] if results else 0
        })

    return search_results

Step 5: Memory Snapshots and Time Travel

async def demonstrate_memory_snapshots(memory, stored_keys):
    """Demonstrate reversible memory operations."""

    print("\n=== Memory Snapshot Demonstration ===")

    # Create initial snapshot
    initial_snapshot = await memory.create_memory_snapshot()
    print(f"Created initial snapshot: {initial_snapshot}")

    # Verify initial state
    initial_fact = await memory.retrieve("quantum_computing_1")
    print(f"Initial fact: {initial_fact[:50]}...")

    # Modify memory (simulate corruption or updates)
    print("\nModifying memory state...")

    # Update existing facts
    await memory.store(
        key="quantum_computing_1",
        value="CORRUPTED: This fact has been corrupted",
        quantum_enhanced=False
    )

    # Add incorrect facts
    await memory.store(
        key="wrong_fact_1",
        value="The sky is green and grass is blue",
        quantum_enhanced=False,
        metadata={'category': 'incorrect', 'corrupted': True}
    )

    # Verify corrupted state
    corrupted_fact = await memory.retrieve("quantum_computing_1")
    wrong_fact = await memory.retrieve("wrong_fact_1")
    print(f"Corrupted fact: {corrupted_fact}")
    print(f"Wrong fact: {wrong_fact}")

    # Create snapshot of corrupted state
    corrupted_snapshot = await memory.create_memory_snapshot()
    print(f"Created corrupted snapshot: {corrupted_snapshot}")

    # Restore to initial state
    print("\nRestoring to initial state...")
    restore_success = await memory.restore_memory_snapshot(initial_snapshot)

    if restore_success:
        print("Memory restoration successful!")

        # Verify restoration
        restored_fact = await memory.retrieve("quantum_computing_1")
        wrong_fact_check = await memory.retrieve("wrong_fact_1")

        print(f"Restored fact: {restored_fact[:50]}...")
        print(f"Wrong fact exists: {wrong_fact_check is not None}")

        # Get memory statistics
        stats = await memory.get_stats()
        print(f"Memory coherence after restoration: {stats.get('avg_coherence', 'N/A')}")
    else:
        print("Memory restoration failed!")

    return {
        'initial_snapshot': initial_snapshot,
        'corrupted_snapshot': corrupted_snapshot,
        'restoration_success': restore_success
    }

Step 6: Performance Optimization

async def optimize_memory_performance(memory):
    """Demonstrate memory performance optimization techniques."""

    print("\n=== Memory Performance Optimization ===")

    # Get initial performance metrics
    initial_stats = await memory.get_stats()
    print("Initial Performance Metrics:")
    print(f"  Memory usage: {initial_stats.get('memory_usage', 'N/A')}%")
    print(f"  Average coherence: {initial_stats.get('avg_coherence', 'N/A'):.3f}")
    print(f"  Active entanglements: {initial_stats.get('active_entanglements', 'N/A')}")
    print(f"  Cache hit rate: {initial_stats.get('cache_hit_rate', 'N/A'):.2%}")

    # Optimization 1: Compress classical storage
    print("\nOptimization 1: Compressing classical storage...")
    compression_savings = await memory.compress_classical_storage()
    print(f"Storage compressed, saved: {compression_savings:.1f}%")

    # Optimization 2: Clean up weak entanglements
    print("\nOptimization 2: Cleaning up weak entanglements...")
    cleaned_entanglements = await memory.cleanup_weak_entanglements(threshold=0.3)
    print(f"Cleaned up {cleaned_entanglements} weak entanglements")

    # Optimization 3: Optimize quantum circuits
    print("\nOptimization 3: Optimizing quantum circuits...")
    circuit_optimization = await memory.optimize_quantum_circuits()
    print(f"Circuit optimization: {circuit_optimization['gates_reduced']} gates reduced")

    # Optimization 4: Defragment memory
    print("\nOptimization 4: Defragmenting memory...")
    defrag_result = await memory.defragment_memory()
    print(f"Defragmentation: {defrag_result['fragmentation_reduced']:.1f}% improvement")

    # Get optimized performance metrics
    optimized_stats = await memory.get_stats()
    print("\nOptimized Performance Metrics:")
    print(f"  Memory usage: {optimized_stats.get('memory_usage', 'N/A')}%")
    print(f"  Average coherence: {optimized_stats.get('avg_coherence', 'N/A'):.3f}")
    print(f"  Active entanglements: {optimized_stats.get('active_entanglements', 'N/A')}")
    print(f"  Cache hit rate: {optimized_stats.get('cache_hit_rate', 'N/A'):.2%}")

    # Calculate improvements
    improvements = {
        'memory_usage_reduction': initial_stats.get('memory_usage', 100) - optimized_stats.get('memory_usage', 100),
        'coherence_improvement': optimized_stats.get('avg_coherence', 0) - initial_stats.get('avg_coherence', 0),
        'cache_improvement': optimized_stats.get('cache_hit_rate', 0) - initial_stats.get('cache_hit_rate', 0)
    }

    print("\nPerformance Improvements:")
    for metric, improvement in improvements.items():
        print(f"  {metric}: {improvement:+.2f}")

    return improvements

Step 7: Main Tutorial Function

async def main():
    """Main execution function for Tutorial 2."""

    print("=== QuantumLangChain Tutorial 2: Advanced Quantum Memory ===\n")

    try:
        # Step 1: Create advanced memory system
        print("Step 1: Creating advanced quantum memory...")
        memory = await create_advanced_memory()

        # Step 2: Build knowledge base
        print("\nStep 2: Building knowledge base...")
        stored_keys = await build_knowledge_base(memory)

        # Step 3: Create entanglements
        print("\nStep 3: Creating knowledge entanglements...")
        entanglements = await create_knowledge_entanglements(memory, stored_keys)

        # Step 4: Demonstrate quantum search
        search_results = await demonstrate_quantum_search(memory)

        # Step 5: Test memory snapshots
        snapshot_results = await demonstrate_memory_snapshots(memory, stored_keys)

        # Step 6: Optimize performance
        optimization_results = await optimize_memory_performance(memory)

        print("\n=== Tutorial 2 Complete! ===")
        print("You've mastered advanced quantum memory systems!")

        # Summary
        print("\nKey Achievements:")
        print(f"✓ Stored {sum(len(keys) for keys in stored_keys.values())} knowledge items")
        print(f"✓ Created {len(entanglements)} strategic entanglements")
        print(f"✓ Performed {len(search_results)} quantum searches")
        print(f"✓ Tested memory reversibility with snapshots")
        print(f"✓ Optimized memory performance")

    except Exception as e:
        print(f"Tutorial error: {e}")
        import traceback
        traceback.print_exc()

if __name__ == "__main__":
    asyncio.run(main())

Key Concepts Learned

1. Advanced Memory Configuration: Complex memory setups with optimization
2. Structured Knowledge Storage: Organizing information with metadata
3. Strategic Entanglements: Creating meaningful quantum correlations
4. Quantum Search: Amplitude amplification for enhanced retrieval
5. Memory Reversibility: Snapshots and time-travel capabilities
6. Performance Optimization: Techniques for efficient quantum memory

---

Tutorial 3: Multi-Agent Quantum Collaboration

Overview

Build sophisticated multi-agent systems where AI agents collaborate using quantum entanglement and interference patterns.

Learning Objectives

• Design specialized agent roles
• Implement quantum entanglement between agents
• Use quantum interference for consensus building
• Create belief propagation networks
• Optimize collaborative performance

Step 1: Agent Role Design

import asyncio
import numpy as np
from dataclasses import dataclass
from enum import Enum
from typing import Dict, List, Any
from quantumlangchain import EntangledAgents, AgentRole, QuantumConfig
from quantumlangchain.backends import QiskitBackend

class AgentSpecialization(Enum):
    RESEARCHER = "researcher"
    ANALYST = "analyst"
    CREATIVE = "creative"
    CRITIC = "critic"
    SYNTHESIZER = "synthesizer"

@dataclass
class AgentPersonality:
    risk_tolerance: float      # 0.0 (conservative) to 1.0 (aggressive)
    creativity_level: float    # 0.0 (logical) to 1.0 (creative)
    collaboration_style: float # 0.0 (independent) to 1.0 (collaborative)
    expertise_depth: float     # 0.0 (generalist) to 1.0 (specialist)

async def design_agent_roles():
    """Design specialized agent roles for quantum collaboration."""

    agent_configs = [
        {
            'agent_id': 'dr_quantum',
            'role': AgentRole.RESEARCHER,
            'specialization': AgentSpecialization.RESEARCHER,
            'capabilities': [
                'literature_review',
                'hypothesis_generation',
                'experimental_design',
                'quantum_theory_analysis'
            ],
            'personality': AgentPersonality(
                risk_tolerance=0.3,
                creativity_level=0.7,
                collaboration_style=0.8,
                expertise_depth=0.9
            ),
            'quantum_weight': 1.0,
            'knowledge_domains': ['quantum_physics', 'quantum_computing', 'mathematics'],
            'reasoning_style': 'analytical_deductive'
        },
        {
            'agent_id': 'ada_ml',
            'role': AgentRole.ANALYST,
            'specialization': AgentSpecialization.ANALYST,
            'capabilities': [
                'data_analysis',
                'pattern_recognition',
                'statistical_modeling',
                'ml_algorithm_optimization'
            ],
            'personality': AgentPersonality(
                risk_tolerance=0.5,
                creativity_level=0.6,
                collaboration_style=0.9,
                expertise_depth=0.8
            ),
            'quantum_weight': 0.9,
            'knowledge_domains': ['machine_learning', 'statistics', 'data_science'],
            'reasoning_style': 'empirical_inductive'
        },
        {
            'agent_id': 'leo_creative',
            'role': AgentRole.CREATIVE,
            'specialization': AgentSpecialization.CREATIVE,
            'capabilities': [
                'brainstorming',
                'metaphorical_thinking',
                'cross_domain_synthesis',
                'innovative_solutions'
            ],
            'personality': AgentPersonality(
                risk_tolerance=0.8,
                creativity_level=0.9,
                collaboration_style=0.7,
                expertise_depth=0.5
            ),
            'quantum_weight': 0.8,
            'knowledge_domains': ['design_thinking', 'philosophy', 'art', 'innovation'],
            'reasoning_style': 'associative_creative'
        },
        {
            'agent_id': 'clara_critic',
            'role': AgentRole.ANALYST,
            'specialization': AgentSpecialization.CRITIC,
            'capabilities': [
                'critical_analysis',
                'risk_assessment',
                'logical_validation',
                'weakness_identification'
            ],
            'personality': AgentPersonality(
                risk_tolerance=0.2,
                creativity_level=0.3,
                collaboration_style=0.6,
                expertise_depth=0.7
            ),
            'quantum_weight': 0.7,
            'knowledge_domains': ['logic', 'critical_thinking', 'risk_management'],
            'reasoning_style': 'skeptical_analytical'
        },
        {
            'agent_id': 'sophia_synthesizer',
            'role': AgentRole.RESEARCHER,
            'specialization': AgentSpecialization.SYNTHESIZER,
            'capabilities': [
                'information_synthesis',
                'consensus_building',
                'holistic_integration',
                'decision_optimization'
            ],
            'personality': AgentPersonality(
                risk_tolerance=0.5,
                creativity_level=0.6,
                collaboration_style=1.0,
                expertise_depth=0.6
            ),
            'quantum_weight': 0.95,
            'knowledge_domains': ['systems_thinking', 'decision_theory', 'integration'],
            'reasoning_style': 'holistic_synthetic'
        }
    ]

    print("Designed 5 specialized agent roles:")
    for config in agent_configs:
        print(f"  {config['agent_id']}: {config['specialization'].value} "
              f"(Quantum weight: {config['quantum_weight']})")

    return agent_configs

Step 2: Initialize Quantum Agent System

async def initialize_quantum_agents(agent_configs):
    """Initialize the quantum agent collaboration system."""

    # Create quantum configuration optimized for multi-agent systems
    config = QuantumConfig(
        num_qubits=8,           # More qubits for complex entanglements
        circuit_depth=10,       # Deeper circuits for agent interactions
        decoherence_threshold=0.08,  # Strict coherence for collaboration
        backend_type="qiskit",
        shots=2048,
        optimization_level=3,
        enable_error_correction=True
    )

    # Initialize quantum backend
    backend = QiskitBackend(config=config)
    await backend.initialize()

    # Create entangled agent system
    agents = EntangledAgents(
        agent_configs=agent_configs,
        backend=backend,
        collaboration_config={
            'enable_quantum_interference': True,
            'belief_propagation': True,
            'consensus_threshold': 0.7,
            'max_collaboration_rounds': 5,
            'entanglement_decay_rate': 0.1,
            'interference_strength': 0.8
        }
    )

    # Initialize the agent system
    await agents.initialize()

    print(f"Quantum agent system initialized with {len(agent_configs)} agents")
    return agents

Step 3: Complex Problem Solving

async def collaborative_problem_solving(agents):
    """Demonstrate collaborative quantum problem solving."""

    # Define a complex multi-faceted problem
    complex_problem = """
    Challenge: Design a quantum-enhanced recommendation system for personalized education.

    Requirements:
    1. Leverage quantum machine learning for pattern recognition
    2. Protect student privacy using quantum cryptography
    3. Adapt to individual learning styles and pace
    4. Scale to millions of users efficiently
    5. Provide explainable recommendations
    6. Ensure fairness and avoid bias

    Constraints:
    - Limited quantum hardware availability
    - Regulatory compliance (GDPR, COPPA)
    - Budget constraints
    - 18-month development timeline

    Consider: Technical feasibility, market viability, ethical implications, and implementation strategy.
    """

    print("=== Collaborative Problem Solving ===")
    print(f"Problem: {complex_problem[:100]}...")

    # Collaborative solution with quantum enhancement
    result = await agents.collaborative_solve(
        problem=complex_problem,
        max_iterations=4,
        enable_interference=True,
        quantum_enhanced=True,
        collaboration_strategy='quantum_consensus'
    )

    print(f"\n--- Collaborative Solution ---")
    print(f"Final Solution: {result['solution']}")
    print(f"Confidence Level: {result['confidence']:.2%}")
    print(f"Consensus Score: {result['consensus_score']:.3f}")
    print(f"Quantum Coherence: {result['quantum_coherence']:.3f}")
    print(f"Collaboration Rounds: {result['iterations_used']}")

    # Analyze individual agent contributions
    print(f"\n--- Agent Contributions ---")
    for agent_id, contribution in result['contributions'].items():
        print(f"\n{agent_id}:")
        print(f"  Perspective: {contribution['perspective']}")
        print(f"  Key Insights: {contribution['insights'][:200]}...")
        print(f"  Confidence: {contribution['confidence']:.2%}")
        print(f"  Influence on Final Solution: {contribution['influence_score']:.3f}")

    return result

Step 4: Belief Propagation Network

from quantumlangchain.agents import BeliefState

async def demonstrate_belief_propagation(agents):
    """Demonstrate quantum belief propagation between agents."""

    print("\n=== Quantum Belief Propagation ===")

    # Initial belief: "Quantum computing will revolutionize AI within 10 years"
    initial_belief = BeliefState(
        proposition="Quantum computing will achieve significant AI breakthroughs within 10 years",
        confidence=0.6,
        evidence=[
            "Recent advances in quantum error correction",
            "Increased investment in quantum AI research",
            "Theoretical advantages of quantum machine learning"
        ],
        uncertainty_factors=[
            "Hardware scalability challenges",
            "Decoherence and noise issues",
            "Competition from classical AI advances"
        ],
        metadata={
            'domain': 'quantum_ai_future',
            'time_horizon': '10_years',
            'initial_agent': 'external_input'
        }
    )

    print(f"Initial Belief: {initial_belief.proposition}")
    print(f"Initial Confidence: {initial_belief.confidence:.2%}")

    # Propagate belief through agent network
    propagation_result = await agents.propagate_belief(
        belief=initial_belief,
        propagation_method='quantum_interference',
        max_propagation_steps=3,
        enable_belief_evolution=True
    )

    print(f"\n--- Belief Propagation Results ---")
    print(f"Propagation Steps: {len(propagation_result)}")

    for step, beliefs in enumerate(propagation_result):
        print(f"\nStep {step + 1}:")
        for agent_id, belief in beliefs.items():
            print(f"  {agent_id}: Confidence {belief.confidence:.2%}")
            if hasattr(belief, 'reasoning'):
                print(f"    Reasoning: {belief.reasoning[:100]}...")

    # Analyze belief convergence
    final_beliefs = propagation_result[-1]
    confidence_values = [belief.confidence for belief in final_beliefs.values()]

    mean_confidence = np.mean(confidence_values)
    confidence_std = np.std(confidence_values)
    consensus_level = 1.0 - (confidence_std / mean_confidence) if mean_confidence > 0 else 0

    print(f"\n--- Consensus Analysis ---")
    print(f"Final Mean Confidence: {mean_confidence:.2%}")
    print(f"Confidence Standard Deviation: {confidence_std:.3f}")
    print(f"Consensus Level: {consensus_level:.3f}")

    return {
        'initial_belief': initial_belief,
        'propagation_result': propagation_result,
        'consensus_metrics': {
            'mean_confidence': mean_confidence,
            'confidence_std': confidence_std,
            'consensus_level': consensus_level
        }
    }

Step 5: Quantum Interference Patterns

async def analyze_quantum_interference(agents):
    """Analyze quantum interference patterns in agent collaboration."""

    print("\n=== Quantum Interference Analysis ===")

    # Create a scenario with conflicting viewpoints
    conflict_scenario = """
    Scenario: Should AI development be regulated by governments?

    Perspectives:
    - Safety: Regulation prevents dangerous AI development
    - Innovation: Regulation stifles technological progress  
    - Ethics: Regulation ensures ethical AI deployment
    - Economics: Regulation affects competitive advantage
    - Practical: Current regulations are insufficient/outdated
    """

    # Run agents with interference patterns
    interference_result = await agents.analyze_with_interference(
        scenario=conflict_scenario,
        interference_types=['constructive', 'destructive', 'neutral'],
        measurement_basis='consensus_vector',
        quantum_enhanced=True
    )

    print(f"Scenario: {conflict_scenario[:60]}...")

    # Analyze interference patterns
    print(f"\n--- Interference Pattern Analysis ---")

    for pattern_type, results in interference_result['interference_patterns'].items():
        print(f"\n{pattern_type.title()} Interference:")
        print(f"  Amplified Viewpoints: {results['amplified_perspectives']}")
        print(f"  Suppressed Viewpoints: {results['suppressed_perspectives']}")
        print(f"  Coherence Level: {results['coherence_level']:.3f}")
        print(f"  Agreement Score: {results['agreement_score']:.3f}")

    # Quantum measurement results
    measurements = interference_result['quantum_measurements']
    print(f"\n--- Quantum Measurements ---")
    print(f"Measurement Basis: {measurements['basis']}")
    print(f"Collapsed State: {measurements['final_state']}")
    print(f"Measurement Probability: {measurements['probability']:.3f}")
    print(f"Quantum Advantage: {measurements['quantum_advantage_score']:.3f}")

    return interference_result

Step 6: Adaptive Agent Learning

async def demonstrate_adaptive_learning(agents):
    """Demonstrate how agents adapt and learn from collaboration."""

    print("\n=== Adaptive Agent Learning ===")

    # Sequence of related problems for learning
    learning_problems = [
        {
            'problem': "How can quantum computing improve drug discovery?",
            'domain': 'quantum_chemistry',
            'complexity': 'medium'
        },
        {
            'problem': "What are the challenges in scaling quantum drug discovery algorithms?",
            'domain': 'quantum_scaling',
            'complexity': 'high'
        },
        {
            'problem': "Design a business model for quantum-enhanced pharmaceutical research.",
            'domain': 'quantum_business',
            'complexity': 'high'
        }
    ]

    learning_history = []

    for i, problem_spec in enumerate(learning_problems, 1):
        print(f"\n--- Learning Problem {i}: {problem_spec['domain']} ---")

        # Get initial agent performance metrics
        initial_metrics = await agents.get_performance_metrics()

        # Solve problem collaboratively
        solution = await agents.collaborative_solve(
            problem=problem_spec['problem'],
            max_iterations=3,
            enable_learning=True,
            learning_rate=0.1,
            experience_weight=0.8
        )

        # Get post-solution metrics
        final_metrics = await agents.get_performance_metrics()

        # Calculate learning improvements
        learning_gains = {}
        for agent_id in initial_metrics.keys():
            if agent_id in final_metrics:
                initial = initial_metrics[agent_id]
                final = final_metrics[agent_id]

                learning_gains[agent_id] = {
                    'confidence_gain': final['avg_confidence'] - initial['avg_confidence'],
                    'coherence_improvement': final['quantum_coherence'] - initial['quantum_coherence'],
                    'collaboration_improvement': final['collaboration_score'] - initial['collaboration_score']
                }

        print(f"Solution Quality: {solution['confidence']:.2%}")
        print(f"Learning Gains:")

        for agent_id, gains in learning_gains.items():
            print(f"  {agent_id}:")
            print(f"    Confidence: {gains['confidence_gain']:+.3f}")
            print(f"    Coherence: {gains['coherence_improvement']:+.3f}")
            print(f"    Collaboration: {gains['collaboration_improvement']:+.3f}")

        learning_history.append({
            'problem': problem_spec,
            'solution': solution,
            'learning_gains': learning_gains,
            'iteration': i
        })

    # Analyze overall learning trajectory
    print(f"\n--- Learning Trajectory Analysis ---")

    total_confidence_gains = {}
    total_coherence_gains = {}

    for agent_id in agents.get_agent_ids():
        confidence_gains = [h['learning_gains'][agent_id]['confidence_gain'] 
                          for h in learning_history if agent_id in h['learning_gains']]
        coherence_gains = [h['learning_gains'][agent_id]['coherence_improvement'] 
                         for h in learning_history if agent_id in h['learning_gains']]

        total_confidence_gains[agent_id] = sum(confidence_gains)
        total_coherence_gains[agent_id] = sum(coherence_gains)

        print(f"{agent_id}:")
        print(f"  Total Confidence Gain: {total_confidence_gains[agent_id]:+.3f}")
        print(f"  Total Coherence Gain: {total_coherence_gains[agent_id]:+.3f}")
        print(f"  Learning Trend: {np.polyfit(range(len(confidence_gains)), confidence_gains, 1)[0]:+.3f}")

    return learning_history

Step 7: Main Tutorial Function

async def main():
    """Main execution function for Tutorial 3."""

    print("=== QuantumLangChain Tutorial 3: Multi-Agent Quantum Collaboration ===\n")

    try:
        # Step 1: Design agent roles
        print("Step 1: Designing specialized agent roles...")
        agent_configs = await design_agent_roles()

        # Step 2: Initialize quantum agent system
        print("\nStep 2: Initializing quantum agent system...")
        agents = await initialize_quantum_agents(agent_configs)

        # Step 3: Collaborative problem solving
        print("\nStep 3: Collaborative problem solving...")
        solution_result = await collaborative_problem_solving(agents)

        # Step 4: Belief propagation
        belief_result = await demonstrate_belief_propagation(agents)

        # Step 5: Quantum interference analysis
        interference_result = await analyze_quantum_interference(agents)

        # Step 6: Adaptive learning
        learning_result = await demonstrate_adaptive_learning(agents)

        print("\n=== Tutorial 3 Complete! ===")
        print("You've mastered quantum multi-agent collaboration!")

        # Summary
        print("\nKey Achievements:")
        print(f"✓ Designed {len(agent_configs)} specialized agent roles")
        print(f"✓ Solved complex multi-faceted problems collaboratively")
        print(f"✓ Demonstrated quantum belief propagation")
        print(f"✓ Analyzed quantum interference patterns")
        print(f"✓ Implemented adaptive agent learning")
        print(f"✓ Achieved consensus score: {solution_result['consensus_score']:.3f}")

    except Exception as e:
        print(f"Tutorial error: {e}")
        import traceback
        traceback.print_exc()

if __name__ == "__main__":
    asyncio.run(main())

Key Concepts Learned

1. Agent Role Design: Creating specialized AI agents with distinct capabilities
2. Quantum Entanglement: Connecting agents through quantum correlations
3. Belief Propagation: Spreading and evolving beliefs across agent networks
4. Quantum Interference: Using interference patterns for consensus building
5. Adaptive Learning: Agents improving through collaborative experience
6. Collaborative Problem Solving: Tackling complex multi-faceted challenges

This comprehensive tutorial collection provides hands-on experience with all major aspects of QuantumLangChain, from basic concepts to advanced multi-agent quantum collaboration systems.