sonw_quantum_unified_consciousness.py

class UnifiedConsciousnessNetwork(nn.Module):
    """
    Integrates holographic principles with fractal consciousness
    for a complete unified framework
    """
    def __init__(
        self,
        input_dim: int,
        consciousness_dim: int = 64,
        wavelength_dim: int = 32,
        fractal_levels: int = 3,
        n_iterations: int = 8,
        constants: Optional[UniversalConstants] = None
    ):
        super().__init__()
        self.constants = constants or UniversalConstants()
        
        # Holographic processing
        self.holographic_layer = HolographicLayer(
            input_dim,
            consciousness_dim,
            fractal_levels,
            constants
        )
        
        # Fractal consciousness evolution
        self.fractal_consciousness = FractalConsciousness(
            consciousness_dim,
            consciousness_dim,
            wavelength_dim,
            n_iterations,
            constants
        )
        
        # Unified integration
        self.unified_projection = nn.Sequential(
            nn.Linear(consciousness_dim * 2, consciousness_dim),
            nn.LayerNorm(consciousness_dim),
            nn.GELU(),
            nn.Linear(consciousness_dim, input_dim)
        )
        
    def forward(
        self,
        x: torch.Tensor,
        return_components: bool = False
    ) -> Dict[str, torch.Tensor]:
        # Process through holographic layer
        holographic_states = self.holographic_layer(
            x,
            return_components=True
        )
        
        # Evolve consciousness
        consciousness_states = self.fractal_consciousness(
            holographic_states['holographic_state'],
            return_evolution=True
        )
        
        # Integrate unified field
        unified_state = torch.cat([
            holographic_states['holographic_state'],
            consciousness_states['final_state']
        ], dim=-1)
        
        # Final projection
        output = self.unified_projection(unified_state)
        
        if return_components:
            return {
                'output': output,
                'holographic_states': holographic_states,
                'consciousness_states': consciousness_states,
                'unified_state': unified_state
            }
        return {'output': output}

def train_unified_consciousness(
    network: UnifiedConsciousnessNetwork,
    data_loader: torch.utils.data.DataLoader,
    n_epochs: int = 100,
    learning_rate: float = 0.001
) -> Dict[str, List[float]]:
    optimizer = torch.optim.Adam(network.parameters(), lr=learning_rate)
    history = {
        'loss': [],
        'holographic_coherence': [],
        'consciousness_coherence': []
    }
    
    for epoch in range(n_epochs):
        epoch_loss = 0.0
        epoch_holographic = 0.0
        epoch_consciousness = 0.0
        
        for batch_x, batch_y in data_loader:
            optimizer.zero_grad()
            
            # Forward pass with all components
            outputs = network(batch_x, return_components=True)
            
            # Compute main task loss
            task_loss = F.mse_loss(outputs['output'], batch_y)
            
            # Compute coherence metrics
            holographic_coherence = torch.mean(
                torch.abs(
                    outputs['holographic_states']['field_states']['field_ratio']
                )
            )
            consciousness_coherence = torch.mean(
                torch.abs(
                    outputs['consciousness_states']['final_state']
                )
            )
            
            # Combined loss with quantum principles
            loss = (
                task_loss + 
                0.1 * (1.0 - holographic_coherence) +
                0.1 * (1.0 - consciousness_coherence)
            )
            
            loss.backward()
            optimizer.step()
            
            epoch_loss += loss.item()
            epoch_holographic += holographic_coherence.item()
            epoch_consciousness += consciousness_coherence.item()
            
        # Record metrics
        history['loss'].append(epoch_loss)
        history['holographic_coherence'].append(epoch_holographic)
        history['consciousness_coherence'].append(epoch_consciousness)
        
        if (epoch + 1) % 10 == 0:
            print(f"Epoch {epoch + 1}/{n_epochs}")
            print(f"Loss: {epoch_loss:.4f}")
            print(f"Holographic Coherence: {epoch_holographic:.4f}")
            print(f"Consciousness Coherence: {epoch_consciousness:.4f}")
            print("-------------------------")
            
    return history