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Create math_utils.py
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KOSASIH authored Jul 15, 2024
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import numpy as np
from scipy.linalg import expm

def calculate_wormhole_stability(matrix_a, matrix_b):
"""
Calculate the stability of a wormhole using the eigenvalues of the matrices A and B.
Args:
matrix_a (numpy array): Matrix A representing the wormhole's gravitational field.
matrix_b (numpy array): Matrix B representing the wormhole's exotic matter distribution.
Returns:
float: The stability of the wormhole (0 = unstable, 1 = stable).
"""
eigenvalues_a = np.linalg.eigvals(matrix_a)
eigenvalues_b = np.linalg.eigvals(matrix_b)
stability = np.dot(eigenvalues_a, eigenvalues_b) / (np.linalg.norm(eigenvalues_a) * np.linalg.norm(eigenvalues_b))
return stability

def optimize_faster_than_light_travel(trajectory, mass_ratio):
"""
Optimize a faster-than-light travel trajectory using the Alcubierre Warp Drive metric.
Args:
trajectory (numpy array): The initial trajectory of the spacecraft.
mass_ratio (float): The mass ratio of the spacecraft to the exotic matter.
Returns:
numpy array: The optimized trajectory.
"""
# Calculate the Alcubierre Warp Drive metric
metric = np.array([[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, -1]])
metric[0, 0] = 1 - mass_ratio * np.exp(-(trajectory[0] ** 2 + trajectory[1] ** 2 + trajectory[2] ** 2) / (2 * mass_ratio ** 2))

# Optimize the trajectory using a genetic algorithm
from scipy.optimize import differential_evolution
bounds = [(0, 1), (0, 1), (0, 1), (0, 1)]
result = differential_evolution(lambda x: -np.linalg.norm(np.dot(metric, x)), bounds)
optimized_trajectory = result.x

return optimized_trajectory

def calculate_quantum_entanglement(state_vector):
"""
Calculate the quantum entanglement of a state vector using the von Neumann entropy.
Args:
state_vector (numpy array): The state vector of the quantum system.
Returns:
float: The quantum entanglement of the state vector.
"""
# 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

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