import socket
import threading
import json
import numpy as np
import torch
from torch.nn import Transformer
import torch.optim as optim
from cryptography.fernet import Fernet

# Quantum Computing Modules
try:
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister, execute, Aer
except ImportError:
pass # Qiskit is not available in this environment

class AdvancedQuantumTeleportation:
def __init__(self, qubits=3):
self.qreg = QuantumRegister(qubits)
self.creg = ClassicalRegister(qubits)
self.circuit = QuantumCircuit(self.qreg, self.creg)

def setup_circuit(self):
for i in range(len(self.qreg)):
self.circuit.h(self.qreg[i])
if i self.circuit.cx(self.qreg[i], self.qreg[i + 1])
self.circuit.rz(np.pi / 2, self.qreg[i + 1])
self.circuit.barrier()
self.circuit.cx(self.qreg[0], self.qreg[1])
self.circuit.h(self.qreg[0])
self.circuit.measure([0, 1], [0, 1])
self.circuit.barrier()
self.circuit.cx(self.qreg[1], self.qreg[2])
self.circuit.cz(self.qreg[0], self.qreg[2])

def execute_circuit(self, shots=1024):
backend = Aer.get_backend('qasm_simulator')
job = execute(self.circuit, backend, shots=shots)
result = job.result()
return result.get_counts()

class EntangledMarkovNet:
def __init__(self, size):
self.size = size
self.states = np.zeros(size)

def update_state(self, new_state):
self.states = np.roll(self.states, -1)
self.states[-1] = new_state

def get_probabilities(self):
return self.states / np.sum(self.states)

class QuantumTransformer(torch.nn.Module):
def __init__(self, input_size, output_size):
super(QuantumTransformer, self).__init__()
self.transformer_layer = Transformer(d_model=input_size, nhead=4)
self.output_layer = torch.nn.Linear(input_size, output_size)

def forward(self, x):
x = self.transformer_layer(x, x)
return self.output_layer(x)

def quantum_data_to_tensor(data):
tensor = torch.tensor(list(data.values()), dtype=torch.float32)
return tensor.unsqueeze(0)

class PolyplexAICommunicator:
def __init__(self, encryption_key):
self.encryptor = Fernet(encryption_key)

def encrypt(self, data):
return self.encryptor.encrypt(json.dumps(data).encode('utf-8'))

def decrypt(self, encrypted_data):
return json.loads(self.encryptor.decrypt(encrypted_data).decode('utf-8'))

def main():
quantum_teleportation = AdvancedQuantumTeleportation()
markov_net = EntangledMarkovNet(1024)
transformer_model = QuantumTransformer(input_size=1024, output_size=1024)
optimizer = optim.SGD(transformer_model.parameters(), lr=0.01)

for _ in range(100):
quantum_teleportation.setup_circuit()
quantum_results = quantum_teleportation.execute_circuit()
quantum_tensor = quantum_data_to_tensor(quantum_results)
transformer_output = transformer_model(quantum_tensor)
loss = torch.nn.functional.mse_loss(transformer_output, quantum_tensor)
optimizer.zero_grad()
loss.backward()
optimizer.step()
markov_net.update_state(quantum_results)

if __name__ == "__main__":
main()