optimizing

import numpy as np

# Define the triagram octal numbers
triagram_octal = {
    '0': '000',
    '1': '001',
    '2': '010',
    '3': '011',
    '4': '100',
    '5': '101',
    '6': '110',
    '7': '111'
}

# Generate 3D orbit using hydrogen atom energy levels
def generate_3d_orbit(triagram_seq):
    # Define the energy levels of hydrogen atom orbits
    energy_levels = {
        '1': 's',
        '2': 's',
        '3': 'p',
        '4': 'p',
        '5': 'd',
        '6': 'd',
        '7': 'd'
    }

    # Define the spatial arrangement of energy levels in the 3D orbit
    spatial_arrangement = {
        's': [0, 0, 0],
        'p': [1 / np.sqrt(2), 1 / np.sqrt(2), 0],
        'd': [1 / np.sqrt(2), 0, 1 / np.sqrt(2)]
    }

    # Arrange the triagram sequence in the 3D orbit based on energy levels
    orbit = []
    for triagram in triagram_seq:
        energy_level = energy_levels[triagram]
        orbit.append(spatial_arrangement[energy_level])

    return orbit

# Simulate quantum entanglement between specified qubit pairs
def simulate_entanglement(tensor):
    # Define the entanglement mappings
    entanglement_map = {
        '01': [0, 7],
        '02': [2, 5],
        '03': [3, 4],
        '16': [1, 6]
    }

    # Perform entanglement between the specified qubit pairs
    for pair in entanglement_map.values():
        qubit1 = pair[0]
        qubit2 = pair[1]
        tensor[qubit1], tensor[qubit2] = tensor[qubit2], tensor[qubit1]

    return tensor

# Calculate arc trigonometric functions using numpy
def calculate_arc_trigonometric(tensor):
    # Apply arc trigonometric functions to each element of the tensor
    tensor = np.arcsin(tensor)
    tensor = np.arccos(tensor)
    tensor = np.arctan(tensor)

    return tensor