sonw_quantum_progressive_network.py

class ProgressiveQuantumNetwork(nn.Module):
    """
    Integrates progressive learning from mathematics to quantum mechanics
    """
    def __init__(
        self,
        input_dim: int,
        hidden_dim: int = 64,
        quantum_dim: int = 32,
        wavelength_dim: int = 32,
        n_terms: int = 8,
        constants: Optional[ProgressiveConstants] = None
    ):
        super().__init__()
        self.constants = constants or ProgressiveConstants()
        
        # Progressive layers
        self.summation_layer = InfiniteSummationLayer(
            input_dim,
            hidden_dim,
            n_terms,
            constants
        )
        
        self.gradient_layer = QuantumGradientLayer(
            hidden_dim,
            quantum_dim,
            constants=constants
        )
        
        self.wavelength_layer = WavelengthMappingLayer(
            quantum_dim,
            wavelength_dim,
            constants
        )
        
        # Integration layer
        self.integration_layer = nn.Sequential(
            nn.Linear(wavelength_dim, hidden_dim),
            nn.LayerNorm(hidden_dim),
            nn.GELU(),
            nn.Linear(hidden_dim, input_dim)
        )
        
    def forward(
        self,
        x: torch.Tensor,
        return_components: bool = False
    ) -> Dict[str, torch.Tensor]:
        # Process through progressive layers
        summation = self.summation_layer(x, return_components=True)
        gradient = self.gradient_layer(
            summation['integrated'],
            return_components=True
        )
        wavelength = self.wavelength_layer(
            gradient['quantum_state'],
            return_components=True
        )
        
        # Final integration
        output = self.integration_layer(
            wavelength['quantum_wavelength']
        )
        
        if return_components:
            return {
                'output': output,
                'summation': summation,
                'gradient': gradient,
                'wavelength': wavelength
            }
        return {'output': output}

def train_progressive_network(
    network: ProgressiveQuantumNetwork,
    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': [],
        'summation_coherence': [],
        'quantum_coherence': [],
        'wavelength_coherence': []
    }
    
    for epoch in range(n_epochs):
        epoch_loss = 0.0
        epoch_summation = 0.0
        epoch_quantum = 0.0
        epoch_wavelength = 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
            summation_coherence = torch.mean(
                torch.abs(outputs['summation']['integrated'])
            )
            quantum_coherence = torch.mean(
                torch.abs(outputs['gradient']['quantum_state'])
            )
            wavelength_coherence = torch.mean(
                torch.abs(outputs['wavelength']['quantum_wavelength'])
            )
            
            # Combined loss
            loss = (
                task_loss + 
                0.1 * (1.0 - summation_coherence) +
                0.1 * (1.0 - quantum_coherence) +
                0.1 * (1.0 - wavelength_coherence)
            )
            
            loss.backward()
            optimizer.step()
            
            epoch_loss += loss.item()
            epoch_summation += summation_coherence.item()
            epoch_quantum += quantum_coherence.item()
            epoch_wavelength += wavelength_coherence.item()
            
        # Record metrics
        history['loss'].append(epoch_loss)
        history['summation_coherence'].append(epoch_summation)
        history['quantum_coherence'].append(epoch_quantum)
        history['wavelength_coherence'].append(epoch_wavelength)
        
        if (epoch + 1) % 10 == 0:
            print(f"Epoch {epoch + 1}/{n_epochs}")
            print(f"Loss: {epoch_loss:.4f}")
            print(f"Summation Coherence: {epoch_summation:.4f}")
            print(f"Quantum Coherence: {epoch_quantum:.4f}")
            print(f"Wavelength Coherence: {epoch_wavelength:.4f}")
            print("-------------------------")
            
    return history