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
from qiskit.circuit.library import ZZFeatureMap
import pqcrypto.kyber as kyber
from scipy.integrate import odeint
import matplotlib.pyplot as plt

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

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 = 64
output_dim = 1
model = QuantumEnhancedNN(input_dim, hidden_dim, output_dim)
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
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=32, shuffle=True)
train_model(model, dataloader, criterion, optimizer, num_epochs=100)
model.eval()
with torch.no_grad():
    train_predictions = model(X_tensor)
time_points = np.linspace(0, 100, 10000)
initial_state = [0.0, 1.0, 1.05]
sigma, rho, beta = 10.0, 28.0, 8.0/3.0
solution = odeint(chaotic_system, initial_state, time_points, args=(sigma, rho, beta))
decrypted_message = generate_keypair_and_encrypt(“Secure transmission of astrophysical data”)

fig, ax = plt.subplots(2, 1, figsize=(10, 10))
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])
plt.show()


Here is a continuation that will be more extended, providing a highly complex and comprehensive codebase:


import qiskit
from qiskit import IBMQ, transpile
from qiskit.providers.ibmq import least_busy
from qiskit.visualization import plot_histogram
from qiskit.providers.aer import QasmSimulator
from qiskit.circuit.random import random_circuit

def simulate_quantum_circuit():
    qc = random_circuit(num_qubits=5, depth=4, max_operands=3)
    backend = Aer.get_backend(‘qasm_simulator’)
    transpiled_qc = transpile(qc, backend)
    job = backend.run(transpiled_qc)
    result = job.result()
    counts = result.get_counts()
    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 main():
    IBMQ.load_account()
    provider = IBMQ.get_provider(hub=‘ibm-q’)
    least_busy_device = least_busy(provider.backends(filters=lambda x: x.configuration().n_qubits >= 5 and not x.configuration().simulator and x.status().operational))
    print("Running on current least busy device: ", least_busy_device)
    
    real_backend = least_busy_device
    counts = simulate_quantum_circuit()
    plot_histogram(counts)
    
    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 = 128
    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))

    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].plot(solution[:, 0], solution[:, 1], solution[:, 2])
    ax[1].set_title(“Chaotic System: Lorenz Attractor”)
    plt.show()

if name == “main”:
    main()



from qiskit_machine_learning.algorithms.classifiers import VQC
from qiskit_machine_learning.circuit.library import TwoLayerQNN
from qiskit.algorithms.optimizers import COBYLA
from qiskit_machine_learning import datasets
from qiskit.providers.aer import StatevectorSimulator
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

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

def extended_main():
    least_busy_device = provider.get_backend(‘ibmq_santiago’)
    print("Running on selected least busy device: “, least_busy_device)
    
    real_backend = least_busy_device
    counts = simulate_quantum_circuit()
    plot_histogram(counts)
    
    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 = 196
    model = QuantumEnhancedNN(input_dim, hidden_dim, output_dim)
    
    criterion = nn.MSELoss()
    optimizer = optim.Adam(model.parameters(), lr=0.0002)
    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=128, 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, 200, 20000)
    solution = odeint(chaotic_system, initial_state, time_points, args=(10.0, 28.0, 8.0/3.0))
    
    extra_data, extra_labels = datasets.iris()
    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=(18, 18))
    ax[0].plot(y, label=‘Actual’)
    ax[0].plot(train_predictions.numpy(), label=“Predicted”)
    ax[0].legend()
    ax[0].set_title(“Astrophysical Prediction”)
    ax[1].plot(solution[:, 0], solution[:, 1], solution[:, 2])
    ax[1].set_title(“Chaotic System: Extended Lorenz Attractor”)
    ax[1].legend()
    
    print(f"Train Accuracy: {train_accuracy:.4f}, Test Accuracy: {test_accuracy:.4f}”)
    plt.show()

if name == “main”:
    extended_main()