import sys
import os
import pennylane as qml
import tensorflow as tf
import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
from tensorflow.python.keras.layers import Dense
from example_utils import get_parser
from importance_sampling.training import ConstantTimeImportanceTraining
from pennylane.qnn.keras import KerasLayer

# 重定向stdout以捕获打印输出
class Tee(object):
    def __init__(self, *files):
        self.files = files
    def write(self, obj):
        for f in self.files:
            f.write(obj)
            f.flush()
    def flush(self):
        for f in self.files:
            f.flush()

output_file = open('output.log', 'w', encoding='utf-8')
original_stdout = sys.stdout
sys.stdout = Tee(sys.stdout, output_file)

def save_plot(fig, filename):
    fig.savefig(filename)
    plt.close(fig)

def plot_history(history):
    # 绘制训练和验证准确率
    fig, ax = plt.subplots(figsize=(10, 5))
    ax.plot(history.history['accuracy'])
    ax.plot(history.history['val_accuracy'])
    ax.set_title('Model Accuracy')
    ax.set_ylabel('Accuracy')
    ax.set_xlabel('Epoch')
    ax.legend(['Train', 'Validation'], loc='upper left')
    save_plot(fig, 'accuracy_plot.png')

    # 绘制训练和验证损失
    fig, ax = plt.subplots(figsize=(10, 5))
    ax.plot(history.history['loss'])
    ax.plot(history.history['val_loss'])
    ax.set_title('Model Loss')
    ax.set_ylabel('Loss')
    ax.set_xlabel('Epoch')
    ax.legend(['Train', 'Validation'], loc='upper left')
    save_plot(fig, 'loss_plot.png')

def calculate_accuracy_fluctuation(history):
    val_accuracies = history.history['val_accuracy']
    mean_accuracy = np.mean(val_accuracies)
    fluctuation = np.sum(np.abs(val_accuracies - mean_accuracy))
    return mean_accuracy, fluctuation

class QuantumNeuralNetwork(tf.keras.Model):
    def __init__(self, n_qubits, n_layers, num_classes):
        super(QuantumNeuralNetwork, self).__init__()
        self.n_qubits = n_qubits
        self.n_layers = n_layers
        self.num_classes = num_classes

        self.dev = qml.device('default.qubit', wires=self.n_qubits)

        @qml.qnode(self.dev, interface='tf', diff_method='backprop')
        def quantum_circuit(inputs, weights):
            qml.templates.AngleEmbedding(inputs, wires=range(self.n_qubits))
            qml.templates.StronglyEntanglingLayers(weights, wires=range(self.n_qubits))
            return [qml.expval(qml.PauliZ(i)) for i in range(self.num_classes)]

        self.quantum_circuit = quantum_circuit
        self.weight_shapes = {"weights": (self.n_layers, self.n_qubits, 3)}

        self.qnn_layer = qml.qnn.KerasLayer(
            self.quantum_circuit,
            weight_shapes=self.weight_shapes,
            output_dim=self.num_classes,
            dtype=tf.float32
        )

    def call(self, inputs):
        x = self.qnn_layer(inputs)
        return tf.keras.activations.softmax(x)

if __name__ == "__main__":
    parser = get_parser("Train model on Iris dataset")
    args = parser.parse_args()

    iris = load_iris()
    X = iris['data'].astype(np.float32)
    y = iris['target'].reshape(-1, 1)

    encoder = OneHotEncoder(sparse_output=False)
    y = encoder.fit_transform(y).astype(np.float32)

    x_train, x_test, y_train, y_test = train_test_split(
        X, y, test_size=0.3, random_state=116
    )

    learning_rate = 0.05
    epochs = 100
    batch_size = 8
    num_classes = 3
    n_qubits = 4
    n_layers = 2

    model = QuantumNeuralNetwork(
        n_qubits=n_qubits,
        n_layers=n_layers,
        num_classes=num_classes
    )

    model.compile(
        optimizer=tf.keras.optimizers.SGD(learning_rate=learning_rate),
        loss='categorical_crossentropy',
        metrics=['accuracy']
    )

    def is_quantum_model(model):
        for layer in model.layers:
            if isinstance(layer, KerasLayer):
                return True
        return False

    if is_quantum_model(model):
        print("The model contains a Quantum Neural Network (QNN) layer.")
    else:
        print("The model does not contain a Quantum Neural Network (QNN) layer.")

    print("\nQuantum Circuit Diagram:")
    sample_inputs = np.zeros(n_qubits, dtype=np.float32)
    sample_weights = np.zeros((n_layers, n_qubits, 3), dtype=np.float32)
    print(qml.draw(model.quantum_circuit)(sample_inputs, sample_weights))

    if args.importance_training:
        wrapped_model = ConstantTimeImportanceTraining(model, score="loss")
        history = wrapped_model.fit(
            x_train, y_train,
            batch_size=batch_size,
            epochs=epochs,
            verbose=1,
            validation_data=(x_test, y_test)
        )
    else:
        history = model.fit(
            x_train, y_train,
            batch_size=batch_size,
            epochs=epochs,
            verbose=1,
            validation_data=(x_test, y_test)
        )

    score = model.evaluate(x_test, y_test, verbose=0)
    print('Test loss:', score[0])
    print('Test accuracy:', score[1])

    mean_accuracy, fluctuation = calculate_accuracy_fluctuation(history)
    print('Mean validation accuracy:', mean_accuracy)
    print('Accuracy fluctuation:', fluctuation)

    plot_history(history)

    # 关闭输出文件并恢复stdout
    sys.stdout = original_stdout
    output_file.close()

print("脚本执行完成。查看 'output.log' 获取打印输出，查看当前目录获取保存的图表。")
讲上述的量子神经网络替换为如下代码的形式：
import pennylane as qml
from pennylane import numpy as np

# 定义设备，使用11个量子比特
dev = qml.device("default.qubit", wires=11)


def encode_group(control_qubits, data_qubits, angles_q2, angles_q3):
    """
    模块化方法，用于对一组量子比特进行角度编码。

    参数：
    - control_qubits (list[int]): 控制比特的索引，例如 [0, 1]。
    - data_qubits (dict): 包含 'q2' 和 'q3' 的量子比特索引，例如 {'q2': 2, 'q3': 3}。
    - angles_q2 (list[float]): q2 在不同控制状态下的 Ry 旋转角度。
    - angles_q3 (list[float]): q3 在不同控制状态下的 Ry 旋转角度。
    """
    control_values = [[0, 0], [0, 1], [1, 0], [1, 1]]

    for cv, angle_q2, angle_q3 in zip(control_values, angles_q2, angles_q3):
        # 对 q2 应用 RY 旋转，受 control_qubits 控制，特定于 control_values
        qml.ctrl(qml.RY, control=control_qubits, control_values=cv)(angle_q2, wires=data_qubits['q2'])

        # 对 q3 应用 RY 旋转，受 control_qubits 控制，特定于 control_values
        qml.ctrl(qml.RY, control=control_qubits, control_values=cv)(angle_q3, wires=data_qubits['q3'])


def apply_controlled_swap(swap_qubit, q_swap1, q_swap2):
    """
    模块化方法，用于应用受控 SWAP 门。

    参数：
    - swap_qubit (int): 作为交换门控制比特的量子比特索引。
    - q_swap1 (int): 受交换操作影响的第一个量子比特索引。
    - q_swap2 (int): 受交换操作影响的第二个量子比特索引。
    """
    # 对 swap_qubit 应用 Hadamard 门
    qml.Hadamard(wires=swap_qubit)

    # 应用受控 SWAP 门
    qml.CSWAP(wires=[swap_qubit, q_swap1, q_swap2])

    # 再次对 swap_qubit 应用 Hadamard 门以恢复控制比特
    qml.Hadamard(wires=swap_qubit)


# 定义一个量子节点 (QNode)
@qml.qnode(dev)
def quantum_neural_network(encoding_data):
    """
    量子神经网络的 QNode，实现多组数据的编码和交换操作。

    参数：
    - encoding_data (list[dict]): 每个字典包含一组编码所需的信息，包括：
        - 'data_qubits': 包含 'q2' 和 'q3' 的量子比特索引。
        - 'swap_qubit': 控制交换门的量子比特索引。
        - 'angles_q2': q2 在不同控制状态下的 Ry 旋转角度列表。
        - 'angles_q3': q3 在不同控制状态下的 Ry 旋转角度列表。

    返回：
    - list[float]: 每组交换控制比特的 Pauli-Z 期望值。
    """
    # 控制比特为 q0 和 q1
    control_qubits = [0, 1]

    # 对每组数据进行编码
    for data_set in encoding_data:
        data_qubits = data_set['data_qubits']
        angles_q2 = data_set['angles_q2']
        angles_q3 = data_set['angles_q3']

        encode_group(control_qubits, data_qubits, angles_q2, angles_q3)

    # 对每组数据应用交换操作
    for data_set in encoding_data:
        swap_qubit = data_set['swap_qubit']
        data_qubits = data_set['data_qubits']

        apply_controlled_swap(swap_qubit, data_qubits['q3'], data_qubits['q2'])

    # 测量每个交换控制比特的 Pauli-Z 期望值
    results = [qml.expval(qml.PauliZ(data_set['swap_qubit'])) for data_set in encoding_data]

    return results


# 测试代码
if __name__ == "__main__":
    # 定义多组数据集，每组包含 q2 和 q3 的角度，以及对应的 swap 控制比特
    encoding_data = [
        {
            'data_qubits': {'q2': 2, 'q3': 3},
            'swap_qubit': 4,
            'angles_q2': [1.0, 3.0, 5.0, 7.0],
            'angles_q3': [2.0, 4.0, 6.0, 8.0]
        },
        {
            'data_qubits': {'q2': 5, 'q3': 6},
            'swap_qubit': 7,
            'angles_q2': [1.5, 3.5, 5.5, 7.5],
            'angles_q3': [2.5, 4.5, 6.5, 8.5]
        },
        {
            'data_qubits': {'q2': 8, 'q3': 9},
            'swap_qubit': 10,
            'angles_q2': [2.0, 4.0, 6.0, 8.0],
            'angles_q3': [3.0, 5.0, 7.0, 9.0]
        }
    ]

    # 执行量子神经网络
    result = quantum_neural_network(encoding_data)

    # 打印测量结果
    for i, res in enumerate(result, start=1):
        print(f"测量 swap_qubit 的 Pauli-Z 期望值 (集 {i}): {res}")

    # 绘制并打印量子电路
    drawer = qml.draw(quantum_neural_network)
    print(drawer(encoding_data))
上述代码就是需要替换到的量子神经网络，其本身的定义以及定义方式都给你了。