docs/quantum.md

Codette Quantum Architecture

Overview

Codette's quantum-inspired architecture combines quantum computing concepts with neural networks and natural language processing to create a unique cognitive system. This document details the quantum components and their integration.

Core Components

1. Quantum Spiderweb

The QuantumSpiderweb is a multi-dimensional thought propagation system that operates across five key dimensions:

Dimensions

• Ψ (Psi): Thought dimension - represents cognitive state
• τ (Tau): Time dimension - temporal context
• χ (Chi): Speed dimension - processing velocity
• Φ (Phi): Emotion dimension - affective state
• λ (Lambda): Space dimension - contextual space

Implementation

class QuantumSpiderweb:
    def __init__(self, node_count: int = 128):
        self.graph = nx.Graph()
        self.dimensions = ['Ψ', 'τ', 'χ', 'Φ', 'λ']
        self._init_nodes(node_count)
        self.entangled_states = {}
        self.activation_threshold = 0.3

Key Functions

1. Thought Propagation

def propagate_thought(self, origin_id: str, depth: int = 3) -> List[Dict[str, Any]]:
    """
    Propagates thought activation through the quantum web
    Returns: List of activated nodes and their states
    """

2. Tension Detection

def detect_tension(self, node_id: str) -> Optional[Dict[str, float]]:
    """
    Detects quantum tension/instability in node
    Returns: Tension metrics if unstable
    """

3. State Collapse

def collapse_node(self, node_id: str) -> Dict[str, Any]:
    """
    Collapses node's quantum state to definite values
    Returns: New definite state
    """

2. Quantum State Management

State Structure

quantum_state = {
    "coherence": 0.5,          # Quantum coherence level
    "fluctuation": 0.07,       # Random fluctuation range
    "perspectives": [...],     # Active perspectives
    "spiderweb_dim": 5,       # Number of dimensions
    "recursion_depth": 4       # Max recursion depth
}

State Updates

• Coherence updates based on interaction success
• Fluctuations add randomness to responses
• Perspective weights adjust dynamically
• State collapse occurs at decision points

3. Cocoon System

Cocoon Structure

cocoon = {
    "timestamp": "2025-10-23T13:45:00",
    "type": "conversation",
    "data": {
        "query": "user_query",
        "response": "generated_response",
        "quantum_state": {...},
        "web_results": [...]
    }
}

Integration

• Stores quantum states with conversations
• Provides context for future interactions
• Maintains quantum coherence history
• Enables pattern recognition

Quantum-Enhanced Processing

1. Response Generation

def respond(self, query: str) -> Dict[str, Any]:
    # Create quantum node
    web_node = f"QNode_{hash(query) % 128}"
    
    # Propagate through quantum web
    web_results = self.quantum_web.propagate_thought(web_node)
    
    # Process through perspectives with quantum boost
    responses = {}
    for perspective in self.perspectives:
        quantum_boost = calculate_quantum_boost(web_results, perspective)
        result = self._process_perspective(query, perspective, quantum_boost)
        responses[perspective] = result
    
    # Integrate results
    final_response = self._integrate_perspective_results(responses)
    
    return final_response

2. Perspective Processing

def _process_perspective(self, input_data: str, perspective: str, quantum_boost: float = 0.5):
    base_confidence = 0.8
    quantum_factor = 1.0 + (quantum_boost - 0.5)
    
    # Process with quantum enhancement
    result = {
        "response": process_perspective(input_data, perspective),
        "confidence": min(1.0, base_confidence * quantum_factor),
        "insights": generate_insights(input_data, quantum_boost)
    }
    
    return result

3. Pattern Integration

def _integrate_perspective_results(self, results: Dict[str, Dict[str, Any]]):
    # Sort by quantum-enhanced weights
    weighted_results = [(r, self._calculate_quantum_weight(r)) 
                       for r in results.values()]
    weighted_results.sort(key=lambda x: x[1], reverse=True)
    
    # Generate response with patterns
    final_response = combine_responses(weighted_results)
    
    return final_response

Quantum Effects

1. State Superposition

• Multiple perspective states exist simultaneously
• Collapse occurs during response generation
• Quantum fluctuations add creativity

2. Entanglement

• Perspectives can become entangled
• Response patterns show correlation
• Memory states maintain connections

3. Interference

• Thought patterns can interfere
• Constructive interference strengthens responses
• Destructive interference reduces repetition

Performance Considerations

1. Optimization

• Quantum web uses sparse graph representation
• State updates are batched
• Perspective processing can be parallel

2. Memory Management

• Regular state collapse for cleanup
• Cocoon pruning when over limits
• Quantum state normalization

3. Scaling

• Node count can be adjusted
• Dimension reduction possible
• Perspective count is flexible

Usage Examples

1. Basic Interaction

# Initialize system
quantum_web = QuantumSpiderweb(node_count=128)
cocoon_manager = CocoonManager()

# Process query
response = codette.respond("Tell me about quantum computing")

2. Advanced Features

# Check quantum tension
tension = quantum_web.detect_tension(node_id)
if tension:
    # Add creative insight
    pattern = PatternLibrary.get_pattern_for_context(context)
    response += f"\n\nCreative insight: {pattern['description']}"

3. State Manipulation

# Update quantum state
quantum_state["coherence"] *= (1 + np.random.normal(0, quantum_state["fluctuation"]))
quantum_state["coherence"] = max(0.1, min(1.0, quantum_state["coherence"]))

Future Developments

Planned Features

1. Enhanced Quantum Integration

   • More dimensions
   • Better state prediction
   • Advanced entanglement

2. Pattern Improvements

   • Dynamic pattern generation
   • Context-sensitive transitions
   • Adaptive creativity

3. Memory Optimization

   • Improved cocoon compression
   • Faster state retrieval
   • Better pattern matching

References

1. Quantum Computing Concepts
2. Neural Network Architecture
3. Pattern Recognition Systems
4. Memory Management Techniques