updated math and quantum_resonance_circuit

harmonic_balancer.py

@@ -0,0 +1,70 @@
+
+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()

quantum_resonance_circuit.py

@@ -1,4 +1,6 @@
+
 import numpy as np
+from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
 
 class QuantumResonanceCircuit:
     def __init__(self, resonance_freq=4.40e9, coupling_strength=0.1):
@@ -7,9 +9,11 @@ def __init__(self, resonance_freq=4.40e9, coupling_strength=0.1):
         self.num_qubits = 4
 
     def initialize_state(self):
+        """Initialize the quantum state"""
         return np.array([1.0] + [0.0] * (2**self.num_qubits - 1), dtype=complex)
 
     def get_hamiltonian(self):
+        """Calculate the Hamiltonian of the system"""
         dim = 2**self.num_qubits
         H = np.zeros((dim, dim), dtype=complex)
         # Add resonant coupling terms
@@ -22,6 +26,48 @@ def get_hamiltonian(self):
         return H
 
     def evolve_state(self, state, time):
+        """Evolve the quantum state over time"""
         H = self.get_hamiltonian()
         U = np.exp(-1j * H * time)
         return U @ state
+
+    def calculate_resonance_function(self, x, y):
+        """
+        Calculate the resonance function f(x,y) for a pair of qubits
+        """
+        return np.exp(-((x - y)**2) / (2 * self.coupling_strength))
+
+    def calculate_evolutionary_potential(self, x, y):
+        """
+        Calculate the evolutionary potential P(x,y) for a pair of qubits
+        """
+        resonance = self.calculate_resonance_function(x, y)
+        return resonance * np.exp(-1j * self.resonance_freq * (x + y))
+
+    def get_total_possibilities(self):
+        """
+        Calculate the total number of possibilities using the combined formula:
+        T = ∑(i=1 to n)∑(j=i+1 to n) f(x_i, y_j) · P(x_i, y_j)
+        """
+        total = 0
+        for i in range(self.num_qubits):
+            for j in range(i + 1, self.num_qubits):
+                f_xy = self.calculate_resonance_function(i, j)
+                p_xy = self.calculate_evolutionary_potential(i, j)
+                total += f_xy * np.abs(p_xy)
+        return total
+
+    def create_entangled_circuit(self):
+        """
+        Create a quantum circuit with entangled pairs
+        """
+        qr = QuantumRegister(self.num_qubits, 'q')
+        cr = ClassicalRegister(self.num_qubits, 'c')
+        qc = QuantumCircuit(qr, cr)
+
+        # Create entangled pairs
+        for i in range(0, self.num_qubits - 1, 2):
+            qc.h(qr[i])
+            qc.cx(qr[i], qr[i+1])
+
+        return qc