diff --git a/ailibrary_framework.py b/ailibrary_framework.py
deleted file mode 100644
index adbee05..0000000
--- a/ailibrary_framework.py
+++ /dev/null
@@ -1,104 +0,0 @@
-
-import torch
-from drl_upn_networks import DRLNetwork, UPNNetwork
-from archetype_blender import ArchetypeBlender
-from user_neural_pattern import UserNeuralPattern
-from deep_resonance_learning import DeepResonanceLearning
-
-class AILibrary:
-    def __init__(self, input_dim=128, pattern_dim=64, num_archetypes=3):
-        self.input_dim = input_dim
-        self.pattern_dim = pattern_dim
-        
-        # Initialize all components
-        self.drl_network = DRLNetwork(input_dim, pattern_dim * 2, pattern_dim)
-        self.upn_network = UPNNetwork(input_dim, pattern_dim)
-        self.archetype_blender = ArchetypeBlender(num_archetypes, pattern_dim)
-        self.user_pattern = UserNeuralPattern(input_dim, pattern_dim)
-        self.deep_resonance = DeepResonanceLearning(input_dim, pattern_dim)
-        
-        # Initialize training state
-        self.is_training = False
-        self.training_history = []
-    
-    def process_user_input(self, user_input):
-        """
-        Process user input through the entire AI pipeline
-        
-        Args:
-            user_input (torch.Tensor): Input tensor of shape (input_dim,)
-            
-        Returns:
-            dict: Contains processed patterns and resonance outputs
-        """
-        # Generate user neural pattern
-        user_pattern = self.user_pattern(user_input)
-        
-        # Get resonance pattern
-        resonance = self.deep_resonance(user_input)
-        
-        # Process through DRL network
-        drl_output = self.drl_network(user_input, resonance)
-        
-        # Generate UPN pattern
-        upn_pattern = self.upn_network.generate_pattern(user_input)
-        
-        # Blend archetypes based on patterns
-        archetype_blend = self.archetype_blender.get_harmonic_blend(drl_output)
-        
-        return {
-            "user_pattern": user_pattern,
-            "resonance": resonance,
-            "drl_output": drl_output,
-            "upn_pattern": upn_pattern,
-            "archetype_blend": archetype_blend
-        }
-    
-    def train_step(self, input_batch, target_batch):
-        """
-        Perform a single training step
-        
-        Args:
-            input_batch (torch.Tensor): Batch of input data
-            target_batch (torch.Tensor): Batch of target data
-        """
-        self.is_training = True
-        # Implementation of training logic here
-        results = self.process_user_input(input_batch)
-        self.training_history.append({
-            "input": input_batch,
-            "target": target_batch,
-            "results": results
-        })
-        self.is_training = False
-        return results
-
-    def save_state(self, path):
-        """Save the current state of all components"""
-        state = {
-            "drl_network": self.drl_network.state_dict(),
-            "upn_network": self.upn_network.state_dict(),
-            "user_pattern": self.user_pattern.state_dict(),
-            "deep_resonance": self.deep_resonance.state_dict(),
-            "training_history": self.training_history
-        }
-        torch.save(state, path)
-    
-    def load_state(self, path):
-        """Load a previously saved state"""
-        state = torch.load(path)
-        self.drl_network.load_state_dict(state["drl_network"])
-        self.upn_network.load_state_dict(state["upn_network"])
-        self.user_pattern.load_state_dict(state["user_pattern"])
-        self.deep_resonance.load_state_dict(state["deep_resonance"])
-        self.training_history = state["training_history"]
-
-# Example test function
-def test_ailibrary():
-    """
-    Test the AILibrary framework with sample data
-    """
-    ai_lib = AILibrary()
-    sample_input = torch.randn(128)  # Create sample input
-    results = ai_lib.process_user_input(sample_input)
-    return results
diff --git a/archetype_blender.py b/archetype_blender.py
deleted file mode 100644
index 47b5fe1..0000000
--- a/archetype_blender.py
+++ /dev/null
@@ -1,48 +0,0 @@
-
-import torch
-import torch.nn as nn
-import numpy as np
-
-class ArchetypeBlender:
-    def __init__(self, num_archetypes, feature_dim):
-        self.num_archetypes = num_archetypes
-        self.feature_dim = feature_dim
-        self.archetypes = {}
-        self.blending_network = nn.Sequential(
-            nn.Linear(num_archetypes * feature_dim, feature_dim * 2),
-            nn.ReLU(),
-            nn.Linear(feature_dim * 2, feature_dim),
-            nn.Sigmoid()
-        )
-        
-    def add_archetype(self, name, features):
-        """Add a new archetype with its characteristic features"""
-        if len(features) != self.feature_dim:
-            raise ValueError(f"Features must have dimension {self.feature_dim}")
-        self.archetypes[name] = torch.tensor(features, dtype=torch.float32)
-        
-    def blend_archetypes(self, weights):
-        """Blend multiple archetypes based on weights"""
-        if len(weights) != len(self.archetypes):
-            raise ValueError("Weights must match number of archetypes")
-            
-        combined_features = []
-        for name, w in zip(self.archetypes.keys(), weights):
-            combined_features.append(self.archetypes[name] * w)
-            
-        combined = torch.cat(combined_features)
-        return self.blending_network(combined)
-    
-    def get_harmonic_blend(self, context):
-        """Generate context-aware archetype blend"""
-        weights = torch.softmax(torch.randn(len(self.archetypes)), dim=0)
-        return self.blend_archetypes(weights)
-
-# Example usage
-feature_dim = 64
-blender = ArchetypeBlender(num_archetypes=3, feature_dim=feature_dim)
-
-# Add some example archetypes
-blender.add_archetype("wisdom", torch.randn(feature_dim))
-blender.add_archetype("nurture", torch.randn(feature_dim))
-blender.add_archetype("guidance", torch.randn(feature_dim))
diff --git a/deep_resonance_learning.py b/deep_resonance_learning.py
deleted file mode 100644
index 4367758..0000000
--- a/deep_resonance_learning.py
+++ /dev/null
@@ -1,71 +0,0 @@
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-class DeepResonanceLearning(nn.Module):
-    def __init__(self, input_dim, resonance_dim, num_layers=3):
-        super(DeepResonanceLearning, self).__init__()
-        self.resonance_dim = resonance_dim
-        
-        # Resonance layers
-        self.layers = nn.ModuleList([
-            nn.Linear(input_dim if i == 0 else resonance_dim, resonance_dim)
-            for i in range(num_layers)
-        ])
-        
-        # Harmonic attention mechanism
-        self.harmonic_attention = nn.MultiheadAttention(
-            embed_dim=resonance_dim,
-            num_heads=4,
-            dropout=0.1
-        )
-        
-        # Resonance memory
-        self.memory = {}
-        
-    def resonance_forward(self, x):
-        """Process input through resonance layers"""
-        for layer in self.layers:
-            x = F.relu(layer(x))
-            # Add harmonic oscillation
-            x = x + 0.1 * torch.sin(x * 3.14159)
-        return x
-    
-    def apply_harmonic_attention(self, x):
-        """Apply harmonic attention mechanism"""
-        # Reshape for attention
-        x = x.unsqueeze(0)
-        attn_output, _ = self.harmonic_attention(x, x, x)
-        return attn_output.squeeze(0)
-    
-    def forward(self, x, store_resonance=False):
-        # Generate resonance pattern
-        resonance = self.resonance_forward(x)
-        
-        # Apply harmonic attention
-        resonance = self.apply_harmonic_attention(resonance)
-        
-        if store_resonance:
-            self.store_resonance_pattern(x, resonance)
-            
-        return resonance
-    
-    def store_resonance_pattern(self, input_pattern, resonance):
-        """Store resonance pattern for future reference"""
-        pattern_id = hash(tuple(input_pattern.detach().numpy()))
-        self.memory[pattern_id] = resonance.detach()
-        
-    def get_resonance_pattern(self, input_pattern):
-        """Retrieve stored resonance pattern"""
-        pattern_id = hash(tuple(input_pattern.detach().numpy()))
-        return self.memory.get(pattern_id)
-
-# Example usage
-input_dim = 128
-resonance_dim = 64
-drl = DeepResonanceLearning(input_dim, resonance_dim)
-
-# Process input and generate resonance
-input_data = torch.randn(input_dim)
-resonance_pattern = drl(input_data, store_resonance=True)
diff --git a/drl_upn_networks.py b/drl_upn_networks.py
deleted file mode 100644
index 39b01c2..0000000
--- a/drl_upn_networks.py
+++ /dev/null
@@ -1,41 +0,0 @@
-
-import torch
-import torch.nn as nn
-
-class DRLNetwork(nn.Module):
-    def __init__(self, input_size, hidden_size, output_size):
-        super(DRLNetwork, self).__init__()
-        self.resonance_layer = nn.Sequential(
-            nn.Linear(input_size, hidden_size),
-            nn.ReLU(),
-            nn.Dropout(0.2),
-            nn.Linear(hidden_size, hidden_size // 2),
-            nn.ReLU(),
-            nn.Linear(hidden_size // 2, output_size)
-        )
-        self.pattern_memory = {}
-        
-    def forward(self, x, resonance_pattern=None):
-        if resonance_pattern is not None:
-            x = x * resonance_pattern
-        return self.resonance_layer(x)
-    
-    def store_pattern(self, pattern_id, pattern):
-        self.pattern_memory[pattern_id] = pattern
-
-class UPNNetwork(nn.Module):
-    def __init__(self, input_size, pattern_size):
-        super(UPNNetwork, self).__init__()
-        self.pattern_generator = nn.Sequential(
-            nn.Linear(input_size, pattern_size * 2),
-            nn.ReLU(),
-            nn.Linear(pattern_size * 2, pattern_size),
-            nn.Tanh()
-        )
-        
-    def forward(self, user_input):
-        return self.pattern_generator(user_input)
-    
-    def generate_pattern(self, user_data):
-        with torch.no_grad():
-            return self.forward(user_data)
diff --git a/enhanced_ecosystem_with_psi.py b/enhanced_ecosystem_with_psi.py
deleted file mode 100644
index faad17a..0000000
--- a/enhanced_ecosystem_with_psi.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import numpy as np
-
-class EnhancedHumanQuantumEcosystem:
-    def __init__(self, num_groups=5, network_type='small_world', interaction_strength=0.1):
-        self.num_groups = num_groups
-        self.growth_rates = np.random.uniform(0.01, 0.05, num_groups)
-        self.carrying_capacities = np.random.uniform(0.5, 1.0, num_groups)
-        self.innovation_rates = np.random.uniform(0.001, 0.01, num_groups)
-        self.adaptation_rates = np.random.uniform(0.01, 0.05, num_groups)
-        self.interaction_strength = interaction_strength
-        
-        # Add Psi and golden ratio
-        self.psi = np.random.uniform(0, 2*np.pi, num_groups)  # Random initial phase for each group
-        self.golden_ratio = (1 + np.sqrt(5)) / 2
-        
-    def update(self, state, t):
-        # Existing dynamics
-        growth = self.growth_rates * state * (1 - state / self.carrying_capacities)
-        innovation = self.innovation_rates * state
-        adaptation = self.adaptation_rates * (1 - state)
-        
-        # New dynamics incorporating Psi and golden ratio
-        psi_effect = np.sin(self.psi + t / self.golden_ratio)  # Time-dependent phase
-        balancer_effect = self.interaction_strength * (psi_effect - np.mean(psi_effect))
-        
-        # Update Psi
-        self.psi += 0.1 * np.random.randn(self.num_groups)  # Small random changes in Psi
-        
-        # Combine all effects
-        total_effect = growth + innovation + adaptation + balancer_effect
-        return total_effect
diff --git a/enhanced_ecosystem_with_psi_and_frequency.py b/enhanced_ecosystem_with_psi_and_frequency.py
deleted file mode 100644
index b8c1992..0000000
--- a/enhanced_ecosystem_with_psi_and_frequency.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import numpy as np
-
-class EnhancedHumanQuantumEcosystem:
-    def __init__(self, num_groups=5, interaction_strength=0.1, base_frequency=20):
-        self.num_groups = num_groups
-        self.growth_rates = np.random.uniform(0.01, 0.05, num_groups)
-        self.carrying_capacities = np.random.uniform(0.5, 1.0, num_groups)
-        self.innovation_rates = np.random.uniform(0.001, 0.01, num_groups)
-        self.adaptation_rates = np.random.uniform(0.01, 0.05, num_groups)
-        self.interaction_strength = interaction_strength
-        
-        # Psi and frequency-related parameters
-        self.base_frequency = base_frequency  # Hz, super low frequency
-        self.psi = np.random.uniform(0, 2*np.pi, num_groups)  # Random initial phase for each group
-        self.golden_ratio = (1 + np.sqrt(5)) / 2
-        
-    def update(self, state, t):
-        # Existing dynamics
-        growth = self.growth_rates * state * (1 - state / self.carrying_capacities)
-        innovation = self.innovation_rates * state
-        adaptation = self.adaptation_rates * (1 - state)
-        
-        # New dynamics incorporating Psi and frequency
-        frequency = self.base_frequency * (1 + np.sin(t / self.golden_ratio))  # Time-varying frequency
-        psi_effect = np.sin(self.psi + 2 * np.pi * frequency * t)  # Time-dependent phase
-        balancer_effect = self.interaction_strength * (psi_effect - np.mean(psi_effect))
-        
-        # Update Psi based on the state of the system
-        self.psi += 0.1 * np.tanh(state)  # State-dependent phase adjustment
-        
-        # Combine all effects
-        total_effect = growth + innovation + adaptation + balancer_effect
-        return total_effect
diff --git a/enhanced_quantum_reactor.py b/enhanced_quantum_reactor.py
deleted file mode 100644
index ecbe847..0000000
--- a/enhanced_quantum_reactor.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import numpy as np
-from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister, Aer, execute
-
-class QuantumSystem:
-    def __init__(self, num_qubits):
-        self.num_qubits = num_qubits
-        self.parameters = np.zeros(num_qubits)
-
-    def evolve_state(self, state, steps=1):
-        if not isinstance(state, np.ndarray) or state.shape != (self.num_qubits,):
-            raise ValueError(f"Invalid state vector shape. Expected ({self.num_qubits},), got {state.shape}")
-        for _ in range(steps):
-            state = state * np.exp(-1j * self.parameters)
-        return state / np.linalg.norm(state)
-
-    def update_parameters(self, state, score, transition_constant):
-        normalized_state = np.array(state) / np.linalg.norm(state)
-        self.parameters += transition_constant * score * normalized_state
-
-    def get_parameters(self):
-        return self.parameters
-
-    def reset_parameters(self):
-        self.parameters = np.zeros(self.num_qubits)
-
-class EnhancedQuantumReactor:
-    def __init__(self, num_qubits=4, resonance_phase=4.40*np.pi):
-        self.num_qubits = num_qubits
-        self.resonance_phase = resonance_phase
-        self.quantum_system = QuantumSystem(num_qubits)
-        self.parameters = {
-            "magnetic_field": 0.0,  # Tesla
-            "containment_pressure": 0.0,  # Pascals
-            "plasma_density": 0.0,  # particles/m^3
-            "resonance_stability": 0.0  # dimensionless
-        }
-        
-    def create_resonance_circuit(self):
-        qr = QuantumRegister(self.num_qubits, 'q')
-        cr = ClassicalRegister(self.num_qubits, 'c')
-        qc = QuantumCircuit(qr, cr)
-        
-        # Initialize with superposition
-        qc.h(qr[0])
-        
-        # Apply resonance sequence
-        for i in range(self.num_qubits-1):
-            qc.cp(self.resonance_phase/self.num_qubits, qr[i], qr[i+1])
-            qc.cx(qr[i], qr[i+1])
-            
-        # Add measurement
-        qc.measure(qr, cr)
-        return qc
-    
-    def calculate_containment_stability(self, measurement_counts):
-        total_counts = sum(measurement_counts.values())
-        max_count = max(measurement_counts.values())
-        stability = max_count / total_counts
-        return stability
-    
-    def update_containment_parameters(self, stability):
-        self.parameters["magnetic_field"] = 5.0 * stability
-        self.parameters["containment_pressure"] = 1e6 * np.exp(stability)
-        self.parameters["plasma_density"] = 1e20 * stability
-        self.parameters["resonance_stability"] = stability
-        
-    def run_containment_cycle(self):
-        qc = self.create_resonance_circuit()
-        backend = Aer.get_backend('qasm_simulator')
-        job = execute(qc, backend, shots=1000)
-        result = job.result()
-        counts = result.get_counts(qc)
-        
-        stability = self.calculate_containment_stability(counts)
-        self.update_containment_parameters(stability)
-        
-        initial_state = np.ones(self.num_qubits) / np.sqrt(self.num_qubits)
-        evolved_state = self.quantum_system.evolve_state(initial_state)
-        
-        return {
-            "counts": counts,
-            "stability": stability,
-            "parameters": self.parameters,
-            "quantum_state": evolved_state
-        }
diff --git a/golden_harmonic_lwe.py b/golden_harmonic_lwe.py
deleted file mode 100644
index afd30bd..0000000
--- a/golden_harmonic_lwe.py
+++ /dev/null
@@ -1,79 +0,0 @@
-
-import numpy as np
-import time
-from tqdm import tqdm
-
-class GoldenHarmonicLWE:
-    def __init__(self, n=256, q=4093, sigma=1.0, key_lifetime=3600):
-        self.n = n
-        self.q = q
-        self.sigma = sigma
-        self.phi = (1 + np.sqrt(5)) / 2  # Golden ratio
-        self.base = 32  # Increased base for better granularity
-        self.key_lifetime = key_lifetime
-        self.generate_new_key()
-
-    def generate_new_key(self):
-        self.key = np.random.randint(0, self.q, size=self.n)
-        self.key_generated_time = time.time()
-
-    def is_key_valid(self):
-        return time.time() - self.key_generated_time < self.key_lifetime
-
-    def get_encryption_frequency(self):
-        if not self.is_key_valid():
-            self.generate_new_key()
-        return (np.sum(self.key) % self.q) / self.q * 10 + self.phi  # Combine with golden ratio
-
-    def generate_quantum_signal(self, message):
-        t = np.linspace(0, 1, len(message))
-        freq = self.get_encryption_frequency()
-        return np.sin(2 * np.pi * freq * t) * message
-
-    def encode_message(self, message):
-        quantum_signal = self.generate_quantum_signal(message)
-        encoded = np.rint((quantum_signal + 1) * (self.base - 1) / 2).astype(int)
-        return encoded
-
-    def decode_message(self, encoded):
-        if not self.is_key_valid():
-            raise ValueError("Decryption key has expired")
-        t = np.linspace(0, 1, len(encoded))
-        freq = self.get_encryption_frequency()
-        carrier = np.sin(2 * np.pi * freq * t)
-        decoded_signal = 2 * encoded.astype(float) / (self.base - 1) - 1
-        return (decoded_signal * carrier > 0).astype(int)
-
-def run_test_suite(message_lengths, num_trials):
-    results = []
-    for length in tqdm(message_lengths, desc="Testing message lengths"):
-        success_rates = []
-        for _ in range(num_trials):
-            ghl = GoldenHarmonicLWE(key_lifetime=10)
-            test_message = np.random.randint(0, 2, length)
-            encoded = ghl.encode_message(test_message)
-            decoded = ghl.decode_message(encoded)
-            success_rate = np.mean(test_message == decoded) * 100
-            success_rates.append(success_rate)
-        avg_success_rate = np.mean(success_rates)
-        std_success_rate = np.std(success_rates)
-        results.append((length, avg_success_rate, std_success_rate))
-    return results
-
-if __name__ == "__main__":
-    # Run the test suite
-    message_lengths = [100, 500, 1000, 5000, 10000]
-    num_trials = 50
-
-    print("Running Golden Harmonic LWE Test Suite...")
-    test_results = run_test_suite(message_lengths, num_trials)
-
-    print("
-Test Results:")
-    print("Message Length | Avg Success Rate (%) | Std Dev")
-    print("-------------------------------------------------")
-    for length, avg_rate, std_rate in test_results:
-        print(f"{length:14d} | {avg_rate:19.2f} | {std_rate:7.2f}")
-
-    print("
-Done")
diff --git a/harmonic_balancer.py b/harmonic_balancer.py
deleted file mode 100644
index 94001f9..0000000
--- a/harmonic_balancer.py
+++ /dev/null
@@ -1,71 +0,0 @@
-# algorithm/harmonic_balancer.py
-
-import numpy as np
-import matplotlib.pyplot as plt
-from .quantum_system import QuantumSystem
-from ..utils.helpers import phi_pi_transition, generate_harmony_vector, state_to_dna, count_valid_codons, calculate_gc_content, calculate_base_balance
-
-class HarmonicBalancer:
-    def __init__(self, num_qubits, max_iterations, harmony_memory_size, objective_function=None, convergence_threshold=1e-6):
-        self.num_qubits = num_qubits
-        self.max_iterations = max_iterations
-        self.harmony_memory_size = harmony_memory_size
-        self.harmony_memory = [generate_harmony_vector(num_qubits) for _ in range(harmony_memory_size)]
-        self.best_solution = None
-        self.best_score = -np.inf
-        self.history = {'scores': [], 'states': []}
-        self.quantum_system = QuantumSystem(num_qubits)
-        self.objective_function = objective_function if objective_function else self.default_objective_function
-        self.convergence_threshold = convergence_threshold
-
-    def default_objective_function(self, vector, param=1):
-        return np.sum(vector) * param
-
-    def run_experiment(self):
-        for iteration in range(self.max_iterations):
-            new_harmony_vector = self.generate_new_harmony(transition_constant=0.1)
-            evolved_state = new_harmony_vector
-            score = self.objective_function(evolved_state)
-            self.update_harmony_memory(new_harmony_vector, evolved_state, score)
-            self.history['scores'].append(score)
-            if self.check_convergence():
-                break
-
-    def generate_new_harmony(self, transition_constant):
-        # Generate a new harmony vector based on the transition constant
-        return generate_harmony_vector(self.num_qubits)
-
-    def update_harmony_memory(self, new_vector, evolved_state, score):
-        # Update the harmony memory with the new vector and score
-        if score > self.best_score:
-            self.best_score = score
-            self.best_solution = new_vector
-        self.harmony_memory.append(new_vector)
-        if len(self.harmony_memory) > self.harmony_memory_size:
-            self.harmony_memory.pop(0)
-
-    def check_convergence(self):
-        # Check if the algorithm has converged
-        if len(self.history['scores']) < 2:
-            return False
-        return abs(self.history['scores'][-1] - self.history['scores'][-2]) < self.convergence_threshold
-
-    def apply_golden_harmony(self, R, F, E):
-        # Apply the Golden Harmony Theory Integration
-        return np.sqrt((R * F**2) + E**2)
-
-    def apply_resonance_condition(self, F0, k, m, omega, b):
-        # Apply the Resonance Condition
-        return F0 / np.sqrt((k - m * omega**2)**2 + (b * omega)**2)
-
-    def apply_wave_interference(self, y1, y2):
-        # Apply the Wave Interference
-        return y1 + y2
-
-    def plot_convergence(self):
-        plt.plot(self.history['scores'])
-        plt.title('Convergence of Harmonic Balancer Algorithm')
-        plt.xlabel('Iteration')
-        plt.ylabel('Best Score')
-        plt.grid(True)
-        plt.show()
\ No newline at end of file
diff --git a/harmonic_balancer_modified.py b/harmonic_balancer_modified.py
deleted file mode 100644
index 8895b29..0000000
--- a/harmonic_balancer_modified.py
+++ /dev/null
@@ -1,54 +0,0 @@
-
-# algorithm/harmonic_balancer.py
-
-import numpy as np # type: ignore
-import matplotlib.pyplot as plt # type: ignore
-from quantum_system import QuantumSystem
-from utils import phi_pi_transition, generate_harmony_vector, state_to_dna, count_valid_codons, calculate_gc_content, calculate_base_balance
-
-class HarmonicBalancer:
-    def __init__(self, num_qubits, max_iterations, harmony_memory_size, objective_function=None, convergence_threshold=1e-6):
-        self.num_qubits = num_qubits
-        self.max_iterations = max_iterations
-        self.harmony_memory_size = harmony_memory_size
-        self.harmony_memory = [generate_harmony_vector(num_qubits) for _ in range(harmony_memory_size)]
-        self.best_solution = None
-        self.best_score = -np.inf
-        self.history = {'scores': [], 'states': []}
-        self.quantum_system = QuantumSystem(num_qubits)
-        self.objective_function = objective_function if objective_function else self.default_objective_function
-        self.convergence_threshold = convergence_threshold
-
-    def default_objective_function(self, vector, param=1):
-        return np.sum(vector) * param
-
-    def run_experiment(self):
-        for iteration in range(self.max_iterations):
-            new_harmony_vector = self.generate_new_harmony(transition_constant=0.1)
-            evolved_state = new_harmony_vector
-            score = self.objective_function(evolved_state)
-            self.update_harmony_memory(new_harmony_vector, evolved_state, score)
-            self.history['scores'].append(score)
-            self.history['states'].append(evolved_state)
-            self.quantum_system.update_parameters(evolved_state, score, transition_constant=0.1)
-            
-            if iteration > 0 and abs(self.history['scores'][-1] - self.history['scores'][-2]) < self.convergence_threshold:
-                print(f"Convergence achieved at iteration {iteration}")
-                break
-
-        return self.best_solution, self.best_score
-
-    def generate_new_harmony(self, transition_constant):
-        selected_index = np.random.randint(self.harmony_memory_size)
-        selected_vector = self.harmony_memory[selected_index]
-        perturbation = np.random.normal(0, transition_constant / 10, size=len(selected_vector))
-        new_vector = selected_vector + perturbation
-        new_vector = np.clip(new_vector, 0, 1)
-        return new_vector
-
-    def update_harmony_memory(self, new_harmony_vector, evolved_state, score):
-        if score > self.best_score:
-            self.best_solution = evolved_state
-            self.best_score = score
-        worst_index = np.argmin([self.objective_function(harmony) for harmony in self.harmony_memory])
-        self.harmony_memory[worst_index] = new_harmony_vector
\ No newline at end of file
diff --git a/hybrid_graphene_reactor_simulation.py b/hybrid_graphene_reactor_simulation.py
deleted file mode 100644
index c6c1524..0000000
--- a/hybrid_graphene_reactor_simulation.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import numpy as np
-from quantum_resonance_circuit import QuantumResonanceCircuit
-
-class HybridGrapheneReactor:
-    def __init__(self, num_particles=1000, subspace_dimensions=11, 
-                 interaction_strength=0.1, decoherence_rate=0.01):
-        self.num_particles = num_particles
-        self.subspace_dimensions = subspace_dimensions
-        self.interaction_strength = interaction_strength
-        self.decoherence_rate = decoherence_rate
-        self.circuit = QuantumResonanceCircuit()
-        self.quantum_state = self.circuit.initialize_state()
-        
-    def update_quantum_state(self, dt=1e-12):
-        # Coherent evolution
-        self.quantum_state = self.circuit.evolve_state(self.quantum_state, dt)
-        # Apply decoherence
-        self.apply_decoherence(dt)
-        
-    def apply_decoherence(self, dt):
-        decay = np.exp(-self.decoherence_rate * dt)
-        self.quantum_state *= decay
-        self.quantum_state /= np.linalg.norm(self.quantum_state)
-        
-    def get_system_state(self):
-        return self.quantum_state
diff --git a/objective_function.py b/objective_function.py
deleted file mode 100644
index caa9fb8..0000000
--- a/objective_function.py
+++ /dev/null
@@ -1,4 +0,0 @@
-import numpy as np # type: ignore
-
-def custom_objective_function(vector):
-    return np.sum(vector)
\ No newline at end of file
diff --git a/quantum_containment_algorithm.py b/quantum_containment_algorithm.py
deleted file mode 100644
index d48854f..0000000
--- a/quantum_containment_algorithm.py
+++ /dev/null
@@ -1,41 +0,0 @@
-
-    import numpy as np
-    from qiskit import QuantumCircuit, execute, Aer
-    
-    def quantum_enhanced_containment(plasma_parameters):
-        # This is a simplified placeholder for the quantum algorithm
-        # In a real implementation, this would interface with quantum hardware
-        
-        qc = QuantumCircuit(3, 3)
-        qc.h(0)
-        qc.cx(0, 1)
-        qc.cx(0, 2)
-        qc.measure([0,1,2], [0,1,2])
-        
-        backend = Aer.get_backend('qasm_simulator')
-        job = execute(qc, backend, shots=1000)
-        result = job.result()
-        counts = result.get_counts(qc)
-        
-        # Use the quantum result to optimize containment parameters
-        optimized_parameters = optimize_containment(counts, plasma_parameters)
-        
-        return optimized_parameters
-    
-    def optimize_containment(quantum_result, plasma_parameters):
-        # Placeholder for the classical optimization part
-        # This would use the quantum result to fine-tune containment parameters
-        optimized_parameters = plasma_parameters.copy()
-        # Optimization logic would go here
-        return optimized_parameters
-    
-    # Example usage
-    if __name__ == "__main__":
-        initial_parameters = {
-            "magnetic_field_strength": 5.0,
-            "plasma_temperature": 150e6,  # 150 million Kelvin
-            "plasma_density": 1e20,  # particles per cubic meter
-        }
-        optimized_parameters = quantum_enhanced_containment(initial_parameters)
-        print("Optimized parameters:", optimized_parameters)
-    
\ No newline at end of file
diff --git a/quantum_enhanced_lwe.py b/quantum_enhanced_lwe.py
index 2a41f60..cd8aeab 100644
--- a/quantum_enhanced_lwe.py
+++ b/quantum_enhanced_lwe.py
@@ -90,3 +90,18 @@ def quantum_enhanced_encrypt(self, pk, m):
         c = (b + m * (self.q // 4)) % self.q
         return a, c
 
+    def calculate_entangled_pairs(self):
+        """
+        Calculate the number of entangled pairs and their combinations.
+        """
+        return self.n * (self.n - 1) // 2
+
+    def evaluate_evolutionary_potential(self, x, y):
+        """
+        Evaluate the evolutionary potential of a pair.
+        :param x: First element of the pair
+        :param y: Second element of the pair
+        :return: Probability of successful evolution
+        """
+        return np.random.rand()  # Placeholder for actual probability calculation
+
diff --git a/quantum_reactor_simulation.py b/quantum_reactor_simulation.py
deleted file mode 100644
index a2d8f09..0000000
--- a/quantum_reactor_simulation.py
+++ /dev/null
@@ -1,91 +0,0 @@
-
-import numpy as np
-from scipy.integrate import odeint
-from enhanced_ecosystem_with_psi import EnhancedHumanQuantumEcosystem
-
-class QuantumReactor(EnhancedHumanQuantumEcosystem):
-    def __init__(self, num_particles=1000, subspace_dimensions=11, interaction_strength=0.1, base_frequency=1e15):
-        super().__init__(num_groups=subspace_dimensions, interaction_strength=interaction_strength)
-        self.num_particles = num_particles
-        self.subspace_dimensions = subspace_dimensions
-        self.particle_states = np.random.rand(num_particles, subspace_dimensions)
-        self.fusion_threshold = 0.95
-        self.fusion_energy = 17.6  # MeV for D-T fusion
-
-    def subspace_excitation(self, t):
-        return np.sin(2 * np.pi * self.base_frequency * t + self.psi)
-
-    def quantum_tunneling(self, particle_states):
-        tunneling_prob = np.exp(-1 / (particle_states + 1e-10))
-        return np.random.rand(*particle_states.shape) < tunneling_prob
-
-    def fusion_reaction(self, particle_states):
-        fusion_prob = np.mean(particle_states, axis=1)
-        fusion_events = np.random.rand(self.num_particles) < fusion_prob
-        return fusion_events
-
-    def calculate_energy_output(self, fusion_events):
-        return np.sum(fusion_events) * self.fusion_energy
-
-    def reactor_dynamics(self, t, state):
-        particle_states = state.reshape((self.num_particles, self.subspace_dimensions))
-        
-        # Apply subspace excitation
-        excitation = self.subspace_excitation(t)
-        particle_states *= (1 + 0.1 * excitation)
-
-        # Apply quantum tunneling
-        tunneling = self.quantum_tunneling(particle_states)
-        particle_states[tunneling] += 0.1
-
-        # Apply harmonic balancer effect
-        balancer_effect = 0.1 * np.tanh(particle_states)
-        particle_states += balancer_effect
-
-        # Calculate fusion events
-        fusion_events = self.fusion_reaction(particle_states)
-        
-        # Reset fused particles
-        particle_states[fusion_events] = np.random.rand(np.sum(fusion_events), self.subspace_dimensions)
-
-        # Calculate energy output
-        energy_output = self.calculate_energy_output(fusion_events)
-
-        return particle_states.flatten(), energy_output
-
-    def run_simulation(self, duration, time_steps):
-        t = np.linspace(0, duration, time_steps)
-        initial_state = self.particle_states.flatten()
-
-        def ode_wrapper(state, t):
-            new_state, _ = self.reactor_dynamics(t, state)
-            return new_state
-
-        states = odeint(ode_wrapper, initial_state, t)
-        
-        energy_outputs = []
-        for i in range(len(t)):
-            _, energy = self.reactor_dynamics(t[i], states[i])
-            energy_outputs.append(energy)
-
-        return t, states, energy_outputs
-
-def run_quantum_reactor_simulation():
-    reactor = QuantumReactor()
-    duration = 1e-12  # 1 picosecond
-    time_steps = 1000
-
-    t, states, energy_outputs = reactor.run_simulation(duration, time_steps)
-
-    total_energy = np.sum(energy_outputs)
-    average_power = total_energy / duration
-
-    print(f"Quantum Reactor Simulation Results:")
-    print(f"Total Energy Output: {total_energy:.2f} MeV")
-    print(f"Average Power Output: {average_power:.2f} MeV/s")
-    print(f"Peak Power Output: {np.max(energy_outputs):.2f} MeV/s")
-
-    return t, states, energy_outputs
-
-if __name__ == "__main__":
-    run_quantum_reactor_simulation()
diff --git a/quantum_system.py b/quantum_system.py
deleted file mode 100644
index 9de54ae..0000000
--- a/quantum_system.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import numpy as np
-
-class QuantumSystem:
-    def __init__(self, num_qubits):
-        self.num_qubits = num_qubits
-        self.parameters = np.zeros(num_qubits)
-        self.state = np.zeros((num_qubits, num_qubits))
-        self.initialize_circuit()
-
-    def initialize_circuit(self):
-        # Initialize the hybrid graphene-silicon-diamond quantum circuit
-        self.graphene_layer = np.random.rand(self.num_qubits)
-        self.silicon_interface = np.random.rand(self.num_qubits, self.num_qubits)
-        self.diamond_layer = np.random.rand(self.num_qubits)
-
-    def evolve_state(self, state, steps=1):
-        if state.shape[0] != self.num_qubits:
-            raise ValueError("Invalid state vector shape")
-        evolved_state = state
-        for _ in range(steps):
-            evolved_state = self.apply_quantum_operations(evolved_state)
-        return evolved_state / np.linalg.norm(evolved_state)
-
-    def apply_quantum_operations(self, state):
-        # Apply quantum operations based on the hybrid circuit
-        state = np.dot(self.silicon_interface, state)
-        state = state * self.graphene_layer
-        state = state + self.diamond_layer
-        return state
-
-    def update_parameters(self, state, score, transition_constant):
-        self.parameters += transition_constant * score * state
-
-    def get_parameters(self):
-        """
-        Get the current parameters of the quantum system.
-        
-        Returns:
-            np.ndarray: The current parameters.
-        """
-        return self.parameters
-
-    def reset_parameters(self):
-        """
-        Reset the parameters of the quantum system to zero.
-        """
-        self.parameters = np.zeros(self.num_qubits)
-
-    def encode_dna_sequence(self, sequence):
-        # Encode a DNA sequence into a quantum state
-        dna_mapping = {'A': 0, 'T': 1, 'C': 2, 'G': 3}
-        encoded_state = np.zeros(self.num_qubits)
-        for i, base in enumerate(sequence):
-            encoded_state[i % self.num_qubits] += dna_mapping[base]
-        return encoded_state / np.linalg.norm(encoded_state)
\ No newline at end of file
diff --git a/user_neural_pattern.py b/user_neural_pattern.py
deleted file mode 100644
index ef53721..0000000
--- a/user_neural_pattern.py
+++ /dev/null
@@ -1,25 +0,0 @@
-
-import torch
-import torch.nn as nn
-
-class UserNeuralPattern(nn.Module):
-    def __init__(self, input_dim, pattern_dim):
-        super(UserNeuralPattern, self).__init__()
-        self.pattern_layer = nn.Sequential(
-            nn.Linear(input_dim, pattern_dim),
-            nn.ReLU(),
-            nn.Linear(pattern_dim, pattern_dim // 2),
-            nn.Tanh()
-        )
-        
-    def forward(self, user_input):
-        return self.pattern_layer(user_input)
-
-# Example usage
-input_dim = 128
-pattern_dim = 64
-user_pattern = UserNeuralPattern(input_dim, pattern_dim)
-
-# Generate a pattern for a given user input
-user_input = torch.randn(input_dim)
-pattern = user_pattern(user_input)
diff --git a/utils.py b/utils.py
deleted file mode 100644
index b2218b1..0000000
--- a/utils.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import math
-import numpy as np # type: ignore
-
-BASE_PAIRS = ['A', 'T', 'G', 'C']
-START_CODON = 'ATG'
-STOP_CODONS = ['TAA', 'TAG', 'TGA']
-
-PHI = (1 + math.sqrt(5)) / 2  # Golden ratio
-
-def phi_pi_transition(state, transition_constant):
-    # Example implementation of phi_pi_transition
-    return state * transition_constant
-
-def generate_harmony_vector(size):
-    # Example implementation of generating a harmony vector
-    return np.random.rand(size)
-
-def state_to_dna(state):
-    # Example implementation of converting state to DNA sequence
-    return ''.join(['A' if x < 0.25 else 'C' if x < 0.5 else 'G' if x < 0.75 else 'T' for x in state])
-
-def count_valid_codons(dna_sequence):
-    # Example implementation of counting valid codons
-    return sum(1 for i in range(0, len(dna_sequence), 3) if dna_sequence[i:i+3] in ['ATG', 'TAA', 'TAG', 'TGA'])
-
-def calculate_gc_content(dna_sequence):
-    # Example implementation of calculating GC content
-    return (dna_sequence.count('G') + dna_sequence.count('C')) / len(dna_sequence)
-
-def calculate_base_balance(dna_sequence):
-    # Example implementation of calculating base balance
-    return {
-        'A': dna_sequence.count('A'),
-        'C': dna_sequence.count('C'),
-        'G': dna_sequence.count('G'),
-        'T': dna_sequence.count('T')
-    }
\ No newline at end of file