Create entanglement_model.py

quantum_models/entanglement_model.py

@@ -0,0 +1,84 @@
+import numpy as np
+from scipy.linalg import expm
+
+class EntanglementModel:
+    def __init__(self, num_qubits, entanglement_threshold):
+        self.num_qubits = num_qubits
+        self.entanglement_threshold = entanglement_threshold
+
+    def generate_entangled_state(self, num_qubits):
+        """
+        Generate an entangled state using the GHZ state.
+
+        Args:
+            num_qubits (int): The number of qubits.
+
+        Returns:
+            numpy array: The entangled state.
+        """
+        ghz_state = np.zeros(2 ** num_qubits)
+        ghz_state[0] = 1 / np.sqrt(2)
+        ghz_state[-1] = 1 / np.sqrt(2)
+        return ghz_state
+
+    def calculate_entanglement_entropy(self, state_vector):
+        """
+        Calculate the entanglement entropy using the von Neumann entropy.
+
+        Args:
+            state_vector (numpy array): The state vector.
+
+        Returns:
+            float: The entanglement entropy.
+        """
+        # Calculate the density matrix
+        density_matrix = np.outer(state_vector, state_vector)
+
+        # Calculate the von Neumann entropy
+        entropy = -np.trace(np.dot(density_matrix, np.log2(density_matrix)))
+
+        return entropy
+
+    def optimize_entanglement(self, initial_state, target_state):
+        """
+        Optimize the entanglement using a genetic algorithm.
+
+        Args:
+            initial_state (numpy array): The initial state.
+            target_state (numpy array): The target state.
+
+        Returns:
+            numpy array: The optimized entangled state.
+        """
+        def objective_function(params):
+            # Simulate the entanglement using the GHZ state
+            entangled_state = self.generate_entangled_state(self.num_qubits)
+            # Calculate the objective function (e.g. minimize entanglement entropy)
+            objective = np.abs(np.dot(entangled_state, target_state))
+            return objective
+
+        result = minimize(objective_function, initial_state, method="SLSQP")
+        optimized_state = result.x
+        return optimized_state
+
+    def simulate_entanglement(self, state_vector, time_span):
+        """
+        Simulate the entanglement using the Schrödinger equation.
+
+        Args:
+            state_vector (numpy array): The state vector.
+            time_span (list): The time span for the simulation.
+
+        Returns:
+            numpy array: The simulated entanglement data.
+        """
+        # Define the Schrödinger equation
+        def schrodinger_equation(t, y):
+            dydt = np.zeros_like(y)
+            for i in range(self.num_qubits):
+                dydt[i] = 1j * y[i]
+            return dydt
+
+        # Simulate the entanglement
+        solution = odeint(schrodinger_equation, state_vector, time_span)
+        return solution