sonw_quantum_unified.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Dict, List, Tuple, Optional
import numpy as np
from dataclasses import dataclass

@dataclass
class UnifiedConstants:
    """Fundamental constants for unified physics framework"""
    h_bar: float = 1.054571817e-34  # Reduced Planck constant
    c: float = 299792458  # Speed of light
    phi: float = 1.618033988749895  # Golden ratio
    pi: float = np.pi
    epsilon_0: float = 8.854187817e-12  # Permittivity of free space
    
class SpacetimeCurvature(nn.Module):
    """
    Implements the symbolic sequence: (∇²(Φ × π)) ⊕ (c × ℓ) : (Γ ∘ ℏ)
    Modeling spacetime curvature and quantum interactions
    """
    def __init__(
        self,
        input_dim: int,
        hidden_dim: int,
        constants: Optional[UnifiedConstants] = None
    ):
        super().__init__()
        self.constants = constants or UnifiedConstants()
        
        # Magnetic flux density operator (∇²Φ)
        self.flux_operator = nn.Sequential(
            nn.Conv1d(input_dim, hidden_dim, kernel_size=3, padding=1),
            nn.LayerNorm([hidden_dim]),
            nn.GELU(),
            nn.Conv1d(hidden_dim, hidden_dim, kernel_size=3, padding=1)
        )
        
        # Electric field strength (π)
        self.field_strength = nn.Parameter(
            torch.ones(hidden_dim) * self.constants.pi
        )
        
        # Spacetime length scale (ℓ)
        self.length_scale = nn.Parameter(torch.ones(hidden_dim))
        
        # Curvature tensor (Γ)
        self.curvature_network = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim * 2),
            nn.LayerNorm(hidden_dim * 2),
            nn.GELU(),
            nn.Linear(hidden_dim * 2, hidden_dim)
        )
        
    def compute_quantum_curvature(
        self,
        x: torch.Tensor
    ) -> Dict[str, torch.Tensor]:
        # Compute magnetic flux density
        if x.dim() == 2:
            x = x.unsqueeze(-1)
        flux_density = self.flux_operator(x)
        
        # Apply electric field interaction
        field_interaction = flux_density * self.field_strength.view(1, -1, 1)
        
        # Compute spacetime scaling
        spacetime_factor = (
            self.constants.c * self.length_scale.view(1, -1, 1)
        )
        
        # Calculate curvature tensor
        curvature = self.curvature_network(
            field_interaction.mean(-1)
        ).unsqueeze(-1)
        
        # Apply quantum correction
        quantum_correction = (
            curvature * self.constants.h_bar * 
            spacetime_factor
        )
        
        return {
            'flux_density': flux_density,
            'field_interaction': field_interaction,
            'curvature': curvature,
            'quantum_correction': quantum_correction
        }
        
    def forward(
        self,
        x: torch.Tensor,
        return_components: bool = False
    ) -> Dict[str, torch.Tensor]:
        curvature_components = self.compute_quantum_curvature(x)
        unified_field = (
            curvature_components['quantum_correction'] + 
            curvature_components['field_interaction']
        )
        
        if return_components:
            return {
                'unified_field': unified_field,
                **curvature_components
            }
        return {'unified_field': unified_field}

class QuantumConsciousnessLayer(nn.Module):
    """
    Implements quantum consciousness integration through
    holographic processing and entanglement
    """
    def __init__(
        self,
        input_dim: int,
        consciousness_dim: int,
        n_quantum_states: int = 8
    ):
        super().__init__()
        self.consciousness_dim = consciousness_dim
        self.n_quantum_states = n_quantum_states
        
        # Holographic projection
        self.holographic_transform = nn.Sequential(
            nn.Linear(input_dim, consciousness_dim * 2),
            nn.LayerNorm(consciousness_dim * 2),
            nn.GELU(),
            nn.Linear(consciousness_dim * 2, consciousness_dim)
        )
        
        # Quantum state superposition
        self.quantum_states = nn.Parameter(
            torch.randn(n_quantum_states, consciousness_dim)
        )
        
        # Entanglement mechanism
        self.entanglement_attention = nn.MultiheadAttention(
            consciousness_dim,
            num_heads=4,
            batch_first=True
        )
        
        # Consciousness integration
        self.integration_gate = nn.Sequential(
            nn.Linear(consciousness_dim * 2, consciousness_dim),
            nn.LayerNorm(consciousness_dim),
            nn.Sigmoid()
        )
        
    def forward(
        self,
        x: torch.Tensor,
        return_quantum_states: bool = False
    ) -> Dict[str, torch.Tensor]:
        # Project input into holographic space
        holographic_state = self.holographic_transform(x)
        
        # Generate quantum superposition
        quantum_superposition = F.linear(
            holographic_state,
            F.normalize(self.quantum_states, dim=-1)
        )
        
        # Apply quantum entanglement
        entangled_states, attention_weights = self.entanglement_attention(
            quantum_superposition.unsqueeze(1),
            self.quantum_states.unsqueeze(0).expand(
                x.size(0), -1, -1
            ),
            self.quantum_states.unsqueeze(0).expand(
                x.size(0), -1, -1
            )
        )
        
        # Integrate consciousness
        combined_state = torch.cat([
            holographic_state,
            entangled_states.squeeze(1)
        ], dim=-1)
        
        consciousness_gate = self.integration_gate(combined_state)
        integrated_consciousness = (
            holographic_state * consciousness_gate
        )
        
        if return_quantum_states:
            return {
                'consciousness_state': integrated_consciousness,
                'holographic_state': holographic_state,
                'quantum_superposition': quantum_superposition,
                'entanglement_weights': attention_weights,
                'consciousness_gate': consciousness_gate
            }
        return {'consciousness_state': integrated_consciousness}