A Quantum Consciousness Simulation Framework

import numpy as np
from scipy.integrate import solve_ivp
import networkx as nx

# Physical constants
ℏ = 1.054571817e-34  # Planck constant
kB = 1.380649e-23    # Boltzmann constant
COHERENCE_LENGTH = 1e-6  # Quantum coherence length

class DetailedViewport:
    def __init__(self, position, consciousness_level, initial_state):
        self.position = np.array(position)
        self.C = consciousness_level
        self.ψ = initial_state
        self.energy = np.sum(np.abs(initial_state)**2)
        
    def hamiltonian(self):
        """Quantum Hamiltonian including consciousness effects"""
        H_quantum = -ℏ**2/(2*self.energy) * self.laplacian()
        H_consciousness = self.C * self.potential_term()
        return H_quantum + H_consciousness
    
    def time_evolution(self, t, state):
        """Time evolution including decoherence"""
        H = self.hamiltonian()
        decoherence = self.decoherence_term(state)
        return -1j/ℏ * (H @ state) + decoherence

class EnhancedEntanglementNetwork:
    def __init__(self):
        self.graph = nx.Graph()
        self.coherence_threshold = 0.5
        
    def add_viewport(self, viewport):
        """Add viewport with metadata"""
        self.graph.add_node(id(viewport), 
            viewport=viewport,
            coherence=1.0,
            entanglement_count=0
        )
    
    def calculate_entanglement(self, viewport1, viewport2):
        """Detailed entanglement calculation"""
        ψ1, ψ2 = viewport1.ψ, viewport2.ψ
        C1, C2 = viewport1.C, viewport2.C
        
        # Quantum overlap
        overlap = np.abs(np.vdot(ψ1, ψ2))**2
        
        # Consciousness coupling
        coupling = np.sqrt(C1 * C2)
        
        # Spatial decay
        distance = np.linalg.norm(viewport1.position - viewport2.position)
        spatial_factor = np.exp(-distance/COHERENCE_LENGTH)
        
        return overlap * coupling * spatial_factor

def simulate_network_evolution(network, time_span):
    """Simulate evolution of entire entangled network"""
    results = []
    
    def network_derivative(t, state_vector):
        n_viewports = len(network.graph)
        derivative = np.zeros_like(state_vector)
        
        # Reshape state vector into individual viewport states
        states = state_vector.reshape(n_viewports, -1)
        
        for i, viewport1 in enumerate(network.graph.nodes()):
            # Standard evolution
            derivative[i] = viewport1['viewport'].time_evolution(t, states[i])
            
            # Entanglement effects
            for j, viewport2 in enumerate(network.graph.nodes()):
                if i != j:
                    entanglement = network.calculate_entanglement(
                        viewport1['viewport'], 
                        viewport2['viewport']
                    )
                    derivative[i] += entanglement * (states[j] - states[i])
        
        return derivative.flatten()
    
    # Initial conditions
    initial_state = np.concatenate([
        viewport['viewport'].ψ 
        for viewport in network.graph.nodes()
    ])
    
    # Solve system
    solution = solve_ivp(
        network_derivative,
        time_span,
        initial_state,
        method='RK45',
        rtol=1e-8
    )
    
    return solution

def analyze_coherence_patterns(solution, network):
    """Analyze coherence patterns in simulation results"""
    n_viewports = len(network.graph)
    n_timesteps = len(solution.t)
    
    # Reshape solution into viewport states
    states = solution.y.reshape(n_timesteps, n_viewports, -1)
    
    # Calculate coherence matrix over time
    coherence_evolution = np.zeros((n_timesteps, n_viewports, n_viewports))
    
    for t in range(n_timesteps):
        for i in range(n_viewports):
            for j in range(n_viewports):
                coherence_evolution[t,i,j] = np.abs(
                    np.vdot(states[t,i], states[t,j])
                )
    
    return coherence_evolution

# Example usage:
"""
# Create network
network = EnhancedEntanglementNetwork()

# Add viewports
viewport1 = DetailedViewport([0,0,0], 1.0, initial_state1)
viewport2 = DetailedViewport([1,0,0], 0.8, initial_state2)
network.add_viewport(viewport1)
network.add_viewport(viewport2)

# Simulate
time_span = (0, 10)
solution = simulate_network_evolution(network, time_span)

# Analyze
coherence = analyze_coherence_patterns(solution, network)
"""