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# 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
```python
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**
```python
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**
```python
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**
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
# Initialize system
quantum_web = QuantumSpiderweb(node_count=128)
cocoon_manager = CocoonManager()
# Process query
response = codette.respond("Tell me about quantum computing")
```
### 2. Advanced Features
```python
# 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
```python
# 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 |