import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
from qiskit import Aer, execute, QuantumCircuit, IBMQ
from qiskit.circuit.library import ZZFeatureMap
import pqcrypto.kyber as kyber
from scipy.integrate import odeint
import matplotlib.pyplot as plt
from qiskit.providers.aer import QasmSimulator
from qiskit_machine_learning.algorithms.classifiers import VQC
from qiskit_machine_learning.circuit.library import TwoLayerQNN
from qiskit_machine_learning.datasets import ad_hoc_data
from qiskit.algorithms.optimizers import COBYLA

class QuantumEnhancedNN(nn.Module):
    def init(self, input_dim, hidden_dim, output_dim):
        super(QuantumEnhancedNN, self).init()
        self.fc1 = nn.Linear(input_dim, hidden_dim)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(hidden_dim, output_dim)
    
    def forward(self, x):
        out = self.fc1(x)
        out = self.relu(out)
        out = self.fc2(out)
        return out

def generate_time_series(n_timesteps, n_series):
    np.random.seed(0)
    data = np.random.randn(n_series, n_timesteps)
    return data

def preprocess(data):
    X = data[:, :-1]
    y = data[:, -1]
    return X, y

def encode_features(X, feature_map):
    quantum_backend = Aer.get_backend(‘statevector_simulator’)
    quantum_states = []
    for x in X:
        qc = QuantumCircuit(X.shape[1])
        qc.compose(feature_map.bind_parameters(x), inplace=True)
        result = execute(qc, quantum_backend).result()
        state = result.get_statevector()
        quantum_states.append(state)
    return np.array(quantum_states)

def chaotic_system(state, t, sigma, rho, beta):
    x, y, z = state
    dxdt = sigma * (y - x)
    dydt = x * (rho - z) - y
    dzdt = x * y - beta * z
    return [dxdt, dydt, dzdt]

def train_model(model, dataloader, criterion, optimizer, num_epochs):
    model.train()
    for epoch in range(num_epochs):
        for inputs, labels in dataloader:
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

def generate_keypair_and_encrypt(message):
    public_key, private_key = kyber.generate_keypair()
    cipher_text = kyber.encrypt(public_key, message.encode())
    decrypted_message = kyber.decrypt(private_key, cipher_text).decode()
    return decrypted_message

def simulate_quantum_circuit():
    qc = QuantumCircuit(5)
    qc.h(range(5))
    qc.measure_all()
    sim = QasmSimulator()
    job = execute(qc, sim, shots=1024)
    result = job.result()
    counts = result.get_counts(qc)
    return counts

def train_evaluate_secure_chaotic_model(X_train, y_train, X_test, y_test):
    public_key, private_key = kyber.generate_keypair()
    encrypted_message = kyber.encrypt(public_key, “Training data transmission”.encode())
    decrypted_message = kyber.decrypt(private_key, encrypted_message).decode()
    if decrypted_message == “Training data transmission”:
        train_model(model, dataloader, criterion, optimizer, num_epochs=200)
        model.eval()
        with torch.no_grad():
            test_predictions = model(X_test)
            test_loss = criterion(test_predictions, y_test)
        return test_predictions, test_loss

def quantum_variant_classification(X, y):
    X_scaled = StandardScaler().fit_transform(X)
    X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=0)
    feature_dim = X_train.shape[1]
    qnn = TwoLayerQNN(feature_dim=feature_dim, num_qubits=feature_dim, quantum_instance=Aer.get_backend(‘qasm_simulator’))
    vqc = VQC(optimizer=COBYLA(), feature_map=qnn.feature_map, quantum_instance=Aer.get_backend(‘qasm_simulator’))
    vqc.fit(X_train, y_train)
    train_accuracy = vqc.score(X_train, y_train)
    test_accuracy = vqc.score(X_test, y_test)
    return train_accuracy, test_accuracy

IBMQ.load_account()
provider = IBMQ.get_provider(hub=‘ibm-q’)
least_busy_device = provider.backends(filters=lambda x: x.configuration().n_qubits >= 5 and not x.configuration().simulator and x.status().operational())[0]

feature_dim = 99
feature_map = ZZFeatureMap(feature_dimension=feature_dim, reps=2, entanglement=‘linear’)
data = generate_time_series(feature_dim + 1, 1000)
X, y = preprocess(data)
quantum_states = encode_features(X, feature_map)
X_real = np.real(quantum_states)
input_dim = X_real.shape[1]
hidden_dim = 128
output_dim = 1
model = QuantumEnhancedNN(input_dim, hidden_dim, output_dim)
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.0005)
X_tensor = torch.tensor(X_real, dtype=torch.float32)
y_tensor = torch.tensor(y, dtype=torch.float32).view(-1, 1)
dataset = TensorDataset(X_tensor, y_tensor)
dataloader = DataLoader(dataset, batch_size=64, shuffle=True)
train_predictions, test_loss = train_evaluate_secure_chaotic_model(X_tensor, y_tensor, X_tensor, y_tensor)
initial_state = [0.0, 1.0, 1.05]
time_points = np.linspace(0, 150, 15000)
solution = odeint(chaotic_system, initial_state, time_points, args=(10.0, 28.0, 8.0/3.0))
decrypted_message = generate_keypair_and_encrypt(“Secure transmission of astrophysical data”)
counts = simulate_quantum_circuit()
extra_data, extra_labels = ad_hoc_data(training_size=20, test_size=10, n=2, gap=0.3, plot_data=False)[0:2]
X_scaled = StandardScaler().fit_transform(extra_data)
train_accuracy, test_accuracy = quantum_variant_classification(X_scaled, extra_labels)
fig, ax = plt.subplots(2, 1, figsize=(15, 15))
ax[0].plot(y, label=‘Actual’)
ax[0].plot(train_predictions.numpy(), label=“Predicted”)
ax[0].legend()
ax[1] = plt.subplot(212, projection=‘3d’)
ax[1].plot(solution[:, 0], solution[:, 1], solution[:, 2])
ax[1].set_title(f"Chaotic System: Lorenz Attractor with quantum variant classification accuracy:“
                f” Train Accuracy: {train_accuracy:.4f}, Test Accuracy: {test_accuracy:.4f}“)
plt.show()




import scipy.sparse as sp

def extended_hybrid_model(input_dim, hidden_dim, output_dim):
    model = QuantumEnhancedNN(input_dim, hidden_dim, output_dim)
    return model

def sparsify_matrix_data(data):
    return sp.csc_matrix(data)

def full_simulation_protocol():
    least_busy_device = provider.get_backend(‘ibmq_bogota’)
    print(“Running simulation on least busy device: “, least_busy_device)
    feature_map = ZZFeatureMap(feature_dimension=feature_dim, reps=2, entanglement=‘linear’)
    X_real = np.real(encode_features(X, feature_map))
    input_dim = X_real.shape[1]
    hidden_dim = 256
    model = extended_hybrid_model(input_dim, hidden_dim, output_dim)
    criterion = nn.MSELoss()
    optimizer = optim.Adam(model.parameters(), lr=0.0001)
    X_tensor = torch.tensor(sparsify_matrix_data(X_real).toarray(), dtype=torch.float32)
    y_tensor = torch.tensor(y, dtype=torch.float32).view(-1, 1)
    dataset = TensorDataset(X_tensor, y_tensor)
    dataloader = DataLoader(dataset, batch_size=256, shuffle=True)
    train_predictions, test_loss = train_evaluate_secure_chaotic_model(X_tensor, y_tensor, X_tensor, y_tensor)
    initial_state = [0.0, 1.0, 1.05]
    time_points = np.linspace(0, 250, 25000)
    solution = odeint(chaotic_system, initial_state, time_points, args=(9.0, 30.0, 8.0/3.0))
    decrypted_message = generate_keypair_and_encrypt(“Extended secure transmission of astrophysical data”)
    counts = simulate_quantum_circuit()
    extra_data, extra_labels = ad_hoc_data(training_size=40, test_size=20, n=3, gap=0.6, plot_data=False)[0:2]
    X_scaled = StandardScaler().fit_transform(extra_data)
    train_accuracy, test_accuracy = quantum_variant_classification(X_scaled, extra_labels)
    fig, ax = plt.subplots(2, 1, figsize=(20, 20))
    ax[0].plot(y, label=‘Actual’)
    ax[0].plot(train_predictions.numpy(), label=“Predicted”)
    ax[0].legend()
    ax[0].set_title(“Astrophysical Prediction via Quantum-Enhanced Neural Network”)
    ax[1].plot(solution[:, 0], solution[:, 1], solution[:, 2])
    ax[1].set_title(f"Extended Chaotic System Simulation with Quantum Classification Accuracy:\n”
                    f"Train Accuracy: {train_accuracy:.4f}, Test Accuracy: {test_accuracy:.4f}”)
    ax[1].legend()
    plt.show()

def another_level_of_complexity():
    ad_hoc_data(training_size=10, test_size=5, n=1, plot_data=True)
    least_busy_device = provider.get_backend(‘ibmq_guadalupe’)
    backend = IBMQ.get_provider().get_backend(‘ibmq_qasm_simulator’)
    shots = 8192
    qnn = TwoLayerQNN(feature_dim=99, num_qubits=99, quantum_instance=backend)
    vqc = VQC(optimizer=COBYLA(maxiter=100), feature_map=qnn.feature_map, quantum_instance=Aer.get_backend(‘qasm_simulator’))
    counts = simulate_quantum_circuit()
    plot_histogram(counts)
    revealed_message = generate_keypair_and_encrypt(“Another level of secure data handling”)
    enhance_dim_model = extended_hybrid_model(200, 128, 10)
    sub_protocol()

def sub_protocol():
    X_real = np.real(encode_features(X, ZZFeatureMap(feature_dimension=99, reps=2, entanglement=‘linear’)))
    input_dim = X_real.shape[1]
    hidden_dim = 512
    model = extended_hybrid_model(input_dim, hidden_dim, output_dim)
    criterion = nn.MSELoss()
    optimizer = optim.Adam(model.parameters(), lr=0.0001)
    X_tensor = torch.tensor(sparsify_matrix_data(X_real).toarray(), dtype=torch.float32)
    y_tensor = torch.tensor(y, dtype=torch.float32).view(-1, 1)
    dataset = TensorDataset(X_tensor, y_tensor)
    dataloader = DataLoader(dataset, batch_size=512, shuffle=True)
    train_predictions, test_loss = train_evaluate_secure_chaotic_model(X_tensor, y_tensor, X_tensor, y_tensor)
    initial_state, time_points = [1.0, 1.0, 1.0], np.linspace(0, 500, 50000)
    solution = odeint(chaotic_system, initial_state, time_points, args=(11.0, 35.0, 7.0/2.0))
    extra_data, extra_labels = ad_hoc_data(training_size=80, test_size=40, n=4, plot_data=True)
    X_scaled = StandardScaler().fit_transform(extra_data)
    train_accuracy, test_accuracy = quantum_variant_classification(X_scaled, extra_labels)
    fig, ax = plt.subplots(2, 1, figsize=(25, 25))
    ax[0].plot(y, label=‘Actual’)
    ax[0].plot(train_predictions.numpy(), label=“Predicted”)
    ax[0].legend()
    ax[0].set_title(“Astrophysical Prediction Using Quantum-Enhanced Neural Network”)
    ax[1].plot(solution[:, 0], solution[:, 1], solution[:, 2])
    ax[1].set_title(f"Ultimate Chaotic System Simulation & Quantum Classification Accuracy:\n”
                    f"Train Accuracy: {train_accuracy:.4f}, Test Accuracy: {test_accuracy:.4f}")
    ax[1].legend()
    plt.show()

if name == “main”:
    full_simulation_protocol()
    another_level_of_complexity()