diff --git a/quantum/libraries/quantum_data_preprocessing.py b/quantum/libraries/quantum_data_preprocessing.py
new file mode 100644
index 000000000..711e20339
--- /dev/null
+++ b/quantum/libraries/quantum_data_preprocessing.py
@@ -0,0 +1,88 @@
+# quantum_data_preprocessing.py
+import numpy as np
+from sklearn.preprocessing import MinMaxScaler
+from qiskit import QuantumCircuit
+
+def normalize_data(data):
+    """
+    Normalize the input data to the range [0, 1].
+    
+    Parameters:
+    - data: np.ndarray, input data to normalize
+    
+    Returns:
+    - np.ndarray: Normalized data
+    """
+    scaler = MinMaxScaler()
+    normalized_data = scaler.fit_transform(data)
+    return normalized_data
+
+def encode_data_to_quantum_state(data):
+    """
+    Encode classical data into quantum states using amplitude encoding.
+    
+    Parameters:
+    - data: np.ndarray, normalized data to encode (must be 1D)
+    
+    Returns:
+    - QuantumCircuit: Quantum circuit that prepares the quantum state
+    """
+    if data.ndim != 1:
+        raise ValueError("Data must be a 1D array for amplitude encoding.")
+    
+    # Normalize the data to ensure it sums to 1
+    data = data / np.linalg.norm(data)
+    
+    num_qubits = int(np.ceil(np.log2(len(data))))
+    circuit = QuantumCircuit(num_qubits)
+
+    # Prepare the quantum state using amplitude encoding
+    circuit.initialize(data, range(num_qubits))
+    
+    return circuit
+
+def split_dataset(data, labels, test_size=0.2):
+    """
+    Split the dataset into training and testing sets.
+    
+    Parameters:
+    - data: np.ndarray, input data
+    - labels: np.ndarray, corresponding labels
+    - test_size: float, proportion of the dataset to include in the test split
+    
+    Returns:
+    - tuple: (X_train, X_test, y_train, y_test)
+    """
+    num_samples = data.shape[0]
+    indices = np.arange(num_samples)
+    np.random.shuffle(indices)
+
+    split_index = int(num_samples * (1 - test_size))
+    train_indices = indices[:split_index]
+    test_indices = indices[split_index:]
+
+    X_train, X_test = data[train_indices], data[test_indices]
+    y_train, y_test = labels[train_indices], labels[test_indices]
+
+    return X_train, X_test, y_train, y_test
+
+if __name__ == "__main__":
+    # Example usage of the data preprocessing functions
+    # Sample data
+    data = np.array([[0.1, 0.2], [0.4, 0.5], [0.6, 0.8], [0.9, 0.1]])
+    labels = np.array([0, 1, 0, 1])
+
+    # Normalize the data
+    normalized_data = normalize_data(data)
+    print("Normalized Data:\n", normalized_data)
+
+    # Encode the first sample into a quantum state
+    quantum_circuit = encode_data_to_quantum_state(normalized_data[0])
+    print("Quantum Circuit for Encoding:\n", quantum_circuit)
+
+    # Split the dataset
+    X_train, X_test, y_train, y_test = split_dataset(normalized_data, labels)
+    print("Training Data:\n", X_train)
+    print("Testing Data:\n", X_test)
+    print("Training Labels:\n", y_train)
+    print("Testing Labels:\n", y_test)