import torch
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import pennylane as qml
import pennylane.numpy as qnp
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

class EmulatedQuantumHopfFibration:
def __init__(self, num_qubits=2):
self.num_qubits = num_qubits
self.points = {}
self.edges = {}
self.dev = qml.device('default.qubit', wires=num_qubits)

def add_point(self, point, data):
def qnode(data):
self._apply_amplitude_embedding(data)
self._apply_pauli_x(point)
return self.dev.state

self.points[point] = (qnode, torch.tensor(data))

def entangle_edge(self, point1, point2, data):
def qnode(data):
self._apply_amplitude_embedding(data)
self._apply_pauli_x(point1)
self._apply_pauli_x(point2)
self._apply_cnot_entanglement()
return self.dev.state

self.edges[(point1, point2)] = (qnode, torch.tensor(data))

def fluctuate(self):
for point in self.points:
self.points[point] = self._apply_random_rotation(*self.points[point])
for edge in self.edges:
self.edges[edge] = self._apply_random_rotation(*self.edges[edge])

def turn_on(self):
self._set_state(1)

def turn_off(self):
self._set_state(0)

def visualize(self):
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
t_values = qnp.linspace(0, 2*qnp.pi, 100) # Parameter for points on the circle

for point in self.points:
quantum_point, data = self.points[point]
state = quantum_point(data)
x, y, z = self._state_to_hopf_circle(state, t_values)
ax.plot(x, y, z)

plt.show()

# Other methods remain the same

class CapsuleAI:
def __init__(self, input_size, num_classes, activation_fn):
self.input_size = input_size
self.num_classes = num_classes
self.activation_fn = activation_fn
self.capsule_net = self._create_capsule_network()
self.optimizer = self._create_optimizer()
self.loss_function = SymphonicLoss()
self.pipeline = nn.Sequential(
CapsuleRegularization(),
DynamicRouting()
)
self.hopf_fibration = EmulatedQuantumHopfFibration()

# CapsuleAI methods

# Usage example
input_size = ...
num_classes = ...
activation_fn = ...
train_loader = ...
test_loader = ...
num_epochs = ...

# Create CapsuleAI instance
model = CapsuleAI(input_size, num_classes, activation_fn)

# Train the model
model.train(train_loader, num_epochs)

# Make predictions
predictions = model.predict(test_loader)

# Integration with Emulated Quantum Hopf Fibration
fibration = EmulatedQuantumHopfFibration()
fibration.add_point(...)
fibration.entangle_edge(...)
fibration.fluctuate()
fibration.turn_on()
fibration.turn_off()
fibration.visualize()


class EmulatedQuantumHopfFibration:
def __init__(self, num_qubits=2):
"""
Initialize the Emulated Quantum Hopf Fibration.

Args:
num_qubits (int): Number of qubits for the quantum system.
"""
self.num_qubits = num_qubits
self.points = {}
self.edges = {}
self.dev = qml.device('default.qubit', wires=num_qubits)

def add_point(self, point, data):
"""
Add a point to the Emulated Quantum Hopf Fibration.

Args:
point: Identifier for the point.
data: Input data associated with the point.
"""
# ...

def entangle_edge(self, point1, point2, data):
"""
Entangle two points with an edge in the Emulated Quantum Hopf Fibration.

Args:
point1: First point identifier.
point2: Second point identifier.
data: Input data associated with the edge.
"""
# ...

def fluctuate(self):
"""
Apply random fluctuations to the quantum states in the Emulated Quantum Hopf Fibration.
"""
# ...

def turn_on(self):
"""
Turn on the quantum states in the Emulated Quantum Hopf Fibration.
"""
# ...

def turn_off(self):
"""
Turn off the quantum states in the Emulated Quantum Hopf Fibration.
"""
# ...

def visualize(self):
"""
Visualize the Emulated Quantum Hopf Fibration in 3D space.
"""
# ...