# 量子嵌入层
class QuantumEmbedding(nn.Module):
    def __init__(self, vocab_size, embedding_dim, n_qubits=8, backend='default.qubit'):
        super(QuantumEmbedding, self).__init__()
        self.vocab_size = vocab_size
        self.embedding_dim = embedding_dim
        self.n_qubits = n_qubits
        self.state_size = 2 ** n_qubits  # 256

        self.classical_embedding = nn.Embedding(vocab_size, embedding_dim)

        self.linear = nn.Linear(embedding_dim, self.state_size)

        self.dev = qml.device(backend, wires=n_qubits)

        def circuit(inputs, weights):

            qml.AmplitudeEmbedding(inputs, wires=range(n_qubits), normalize=True)

            qml.StronglyEntanglingLayers(weights, wires=range(n_qubits))

            return [qml.expval(qml.PauliZ(wires=i)) for i in range(n_qubits)]

        weight_shapes = {"weights": (1, n_qubits, 3)}  # (layers, rotations, qubits)

        self.qnode = qml.QNode(circuit, self.dev, interface='torch', diff_method='backprop')

        self.qlayer = TorchLayer(self.qnode, weight_shapes)

    def forward(self, x):

        batch_size, seq_length = x.size()

        embeddings = self.classical_embedding(x)

        embeddings = self.linear(embeddings)

        embeddings = embeddings.type(torch.float32)

        embeddings = F.normalize(embeddings, p=2, dim=-1)

        embeddings = embeddings.view(-1, self.state_size)

        quantum_embeddings = self.qlayer(embeddings)  # [batch_size * seq_length, 8]

        quantum_embeddings = quantum_embeddings.view(batch_size, seq_length, self.n_qubits)
        return quantum_embeddings


class QuantumAttention(nn.Module):
    def __init__(self, embed_size, n_qubits=8, backend='default.qubit'):
        super(QuantumAttention, self).__init__()
        self.embed_size = embed_size  # 应为 n_qubits
        self.n_qubits = n_qubits
        self.backend = backend

        self.dev = qml.device(backend, wires=n_qubits)

        def circuit(inputs, weights):

            query = inputs[:, :self.n_qubits]
            key = inputs[:, self.n_qubits:]

            qml.AngleEmbedding(query, wires=range(self.n_qubits), rotation='Y')
            qml.AngleEmbedding(key, wires=range(self.n_qubits), rotation='Z')

            for i in range(self.n_qubits):
                qml.RY(weights[i], wires=i)
                qml.RZ(weights[i + self.n_qubits], wires=i)

            for i in range(self.n_qubits):
                qml.CNOT(wires=[i, (i + 1) % self.n_qubits])

            return [qml.expval(qml.PauliZ(wires=i)) for i in range(self.n_qubits)]

        weight_shapes = {"weights": 2 * n_qubits}

        self.qnode = qml.QNode(circuit, self.dev, interface='torch', diff_method='backprop')

        self.qlayer = TorchLayer(self.qnode, weight_shapes)

        self.query_proj = nn.Linear(embed_size, n_qubits)
        self.key_proj = nn.Linear(embed_size, n_qubits)

        self.softmax = nn.Softmax(dim=-1)

    def forward(self, queries, keys, values):

        batch_size, seq_length, _ = queries.size()

        queries = self.query_proj(queries)
        keys = self.key_proj(keys)

        queries_expanded = queries.unsqueeze(2).expand(-1, -1, seq_length, -1)
        keys_expanded = keys.unsqueeze(1).expand(-1, seq_length, -1, -1)

        combined = torch.cat((queries_expanded, keys_expanded), dim=-1)

        combined = combined.contiguous().view(-1, 2 * self.n_qubits)

        scores = self.qlayer(combined)

        scores = scores.mean(dim=1)

        attention_scores = scores.view(batch_size, seq_length, seq_length)

        attention_weights = self.softmax(attention_scores)  # [batch_size, seq_length, seq_length]

        context = torch.bmm(attention_weights, values)  # [batch_size, seq_length, embed_size]

        return context, attention_weights

class QLSTM(nn.Module):
    def __init__(self,
                 input_size,
                 hidden_size,
                 n_qubits=4,
                 n_qlayers=1,
                 n_vrotations=3,
                 batch_first=True,
                 return_sequences=False,
                 return_state=False,
                 backend="default.qubit",
                 dropout_rate=dropout_rate):
        super(QLSTM, self).__init__()
        self.n_inputs = input_size
        self.hidden_size = hidden_size
        self.num_layers = n_qlayers  # 记录层数
        self.concat_size = self.n_inputs + self.hidden_size
        self.n_qubits = n_qubits
        self.n_qlayers = n_qlayers
        self.n_vrotations = n_vrotations
        self.backend = backend

        self.batch_first = batch_first
        self.return_sequences = return_sequences
        self.return_state = return_state

        self.wires_forget = [f"wire_forget_{i}" for i in range(self.n_qubits)]
        self.wires_input = [f"wire_input_{i}" for i in range(self.n_qubits)]
        self.wires_update = [f"wire_update_{i}" for i in range(self.n_qubits)]
        self.wires_output = [f"wire_output_{i}" for i in range(self.n_qubits)]
        self.wires_output_h = [f"wire_output_h_{i}" for i in range(self.n_qubits)]

        # 定义量子设备
        self.dev_forget = qml.device(self.backend, wires=self.wires_forget)
        self.dev_input = qml.device(self.backend, wires=self.wires_input)
        self.dev_update = qml.device(self.backend, wires=self.wires_update)
        self.dev_output = qml.device(self.backend, wires=self.wires_output)
        self.dev_output_h = qml.device(self.backend, wires=self.wires_output_h)

        # 初始化 dropout 层
        self.dropout = nn.Dropout(dropout_rate)

        # 实例化 QNodes
        self.qlayer_forget = qml.QNode(self._circuit_forget, self.dev_forget, interface="torch")
        self.qlayer_input = qml.QNode(self._circuit_input, self.dev_input, interface="torch")
        self.qlayer_update = qml.QNode(self._circuit_update, self.dev_update, interface="torch")
        self.qlayer_output = qml.QNode(self._circuit_output, self.dev_output, interface="torch")
        self.qlayer_output_h = qml.QNode(self._circuit_output_h, self.dev_output_h, interface="torch")

        # 定义权重形状
        self.weight_shapes = {"weights": (self.n_qlayers, self.n_vrotations, self.n_qubits)}

        # 定义线性层
        self.clayer_in = nn.Linear(self.concat_size, self.n_qubits)
        self.clayer_out_f = nn.Linear(self.n_qubits, self.hidden_size)
        self.clayer_out_i = nn.Linear(self.n_qubits, self.hidden_size)
        self.clayer_out_g = nn.Linear(self.n_qubits, self.hidden_size)
        self.clayer_out_o = nn.Linear(self.n_qubits, self.hidden_size)

        # 定义 TorchLayer
        self.TorchLayers = {
            'forget': qml.qnn.TorchLayer(self.qlayer_forget, self.weight_shapes),
            'input': qml.qnn.TorchLayer(self.qlayer_input, self.weight_shapes),
            'update': qml.qnn.TorchLayer(self.qlayer_update, self.weight_shapes),
            'output': qml.qnn.TorchLayer(self.qlayer_output, self.weight_shapes),
            'output_h': qml.qnn.TorchLayer(self.qlayer_output_h, self.weight_shapes)
        }

    def vqc(self, features, weights, wires_type):
        # 确保 features 是二维张量 [batch_size, n_qubits]
        if features.dim() == 1:
            features = features.unsqueeze(0)
        if not isinstance(features, torch.Tensor):
            features = torch.tensor(features, dtype=torch.float32)

        # 切片 features
        features = features[:, :self.n_qubits]
        ry_params = torch.atan2(features, torch.ones_like(features))
        rz_params = torch.atan2(features ** 2, torch.ones_like(features))

        # 应用 Hadamard 门
        for i in range(self.n_qubits):
            qml.Hadamard(wires=wires_type[i])

        # 应用 RY 和 RZ 旋转
        for i in range(self.n_qubits):
            qml.RY(ry_params[:, i], wires=wires_type[i])
            qml.RZ(rz_params[:, i], wires=wires_type[i])

        # 创建纠缠
        for i in range(1, 3):
            for j in range(self.n_qubits):
                if j + i < self.n_qubits:
                    qml.CNOT(wires=[wires_type[j], wires_type[j + i]])
                else:
                    qml.CNOT(wires=[wires_type[j], wires_type[j + i - self.n_qubits]])

        # 应用 RX、RY、RZ 旋转
        for layer in range(self.n_qlayers):
            for i in range(self.n_qubits):
                qml.RX(weights[layer, 0, i], wires=wires_type[i])
                qml.RY(weights[layer, 1, i], wires=wires_type[i])
                qml.RZ(weights[layer, 2, i], wires=wires_type[i])

    # 定义不同门的量子电路
    def _circuit_forget(self, inputs, weights):
        self.vqc(inputs, weights, self.wires_forget)
        return [qml.expval(qml.PauliZ(wires=i)) for i in self.wires_forget]

    def _circuit_input(self, inputs, weights):
        self.vqc(inputs, weights, self.wires_input)
        return [qml.expval(qml.PauliZ(wires=i)) for i in self.wires_input]

    def _circuit_update(self, inputs, weights):
        self.vqc(inputs, weights, self.wires_update)
        return [qml.expval(qml.PauliZ(wires=i)) for i in self.wires_update]

    def _circuit_output(self, inputs, weights):
        self.vqc(inputs, weights, self.wires_output)
        return [qml.expval(qml.PauliZ(wires=i)) for i in self.wires_output]

    def _circuit_output_h(self, inputs, weights):
        self.vqc(inputs, weights, self.wires_output_h)
        return [qml.expval(qml.PauliZ(wires=i)) for i in self.wires_output_h]


    def forward(self, x, init_states=None):
        batch_size, seq_length, _ = x.size()

        if init_states is None:
            h_t = torch.zeros(self.num_layers, batch_size, self.hidden_size, dtype=torch.float32, device=x.device)
            c_t = torch.zeros(self.num_layers, batch_size, self.hidden_size, dtype=torch.float32, device=x.device)
        else:
            h_t, c_t = init_states

        for t in range(seq_length):
            x_t = x[:, t, :]  # [batch_size, input_size]
            h_t_last_layer = h_t[-1, :, :]  # [batch_size, hidden_size]

            combined = torch.cat((h_t_last_layer, x_t), dim=1)  # [batch_size, hidden_size + input_size]
            combined = self.clayer_in(combined)  # [batch_size, n_qubits]

            f_t_intermediate = self.TorchLayers['forget'](combined)
            i_t_intermediate = self.TorchLayers['input'](combined)
            g_t_intermediate = self.TorchLayers['update'](combined)
            o_t_intermediate = self.TorchLayers['output'](combined)

            f_t = torch.sigmoid(self.clayer_out_f(f_t_intermediate))  # [batch_size, hidden_size]
            i_t = torch.sigmoid(self.clayer_out_i(i_t_intermediate))
            g_t = torch.tanh(self.clayer_out_g(g_t_intermediate))
            o_t = torch.sigmoid(self.clayer_out_o(o_t_intermediate))

            c_t_new = f_t * c_t[-1, :, :] + i_t * g_t  # [batch_size, hidden_size]
            h_t_new = o_t * torch.tanh(c_t_new)  # [batch_size, hidden_size]
            h_t_new = self.dropout(h_t_new)  # [batch_size, hidden_size]

            h_t = h_t.clone()
            h_t[-1, :, :] = h_t_new
            c_t = c_t.clone()
            c_t[-1, :, :] = c_t_new

        return h_t_last_layer  # [batch_size, hidden_size]


# 集成量子注意力的分类模型
class QShallowClassificationLSTMWithAttention(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_size, n_qubits=8, n_qlayers=1, num_classes=2,
                 dropout_rate=dropout_rate, backend='default.qubit'):
        super().__init__()
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_layers = n_qlayers
        self.num_classes = num_classes

        self.embedding = QuantumEmbedding(vocab_size, embedding_dim, n_qubits, backend)

        self.dropout = nn.Dropout(dropout_rate)

        self.attention = QuantumAttention(embed_size=n_qubits, n_qubits=n_qubits, backend=backend)

        self.lstm = QLSTM(
            input_size=n_qubits,
            hidden_size=hidden_size,
            n_qubits=n_qubits,
            n_qlayers=n_qlayers,
            backend=backend,
            dropout_rate=dropout_rate
        )

        # 线性层，将隐藏状态映射到类别
        self.linear = nn.Linear(hidden_size, num_classes)

    def forward(self, x):
        batch_size = x.shape[0]

        # 嵌入
        x = self.embedding(x)  # [batch_size, sequence_length, n_qubits]
        x = self.dropout(x)

        context, attention_weights = self.attention(x, x,
                                x)  # [batch_size, seq_length, embed_size], [batch_size, seq_length, seq_length]

        x = context  # 或者使用 torch.cat([x, context], dim=-1)

        x = self.dropout(x)

        h0 = torch.zeros(self.lstm.num_layers, batch_size, self.lstm.hidden_size, dtype=torch.float32, device=x.device)
        c0 = torch.zeros(self.lstm.num_layers, batch_size, self.lstm.hidden_size, dtype=torch.float32, device=x.device)

        # LSTM
        outputs = self.lstm(x, (h0, c0))  # [batch_size, hidden_size]

        # 分类
        out = self.linear(outputs)  # [batch_size, num_classes]

        return out我的模型代码中在几个量子模块之间需要进行多次的数据形态从经典态到量子态之间的相互转换，我想要修改代码只在最初输入阶段将数据通过量子嵌入将数据转换成量子态，在最终输出阶段将数据转换成经典态。在模型内部始终保持量子态的数据形式，我是否只需要将量子嵌入层和连自注意力层的量子电路不进行测量，返回量子态的数据形式传递到下一模块进行使用就可以解决问题？或者我将量子嵌入层和连自注意力层的量子电路合并到QLSTM中的量子电路中，构成一个完整的量子电路也可以解决问题。请你仔细阅读我的要求和代码，并且分析出合理正确的解决方案，给出修改后的完整代码，要求尽量不改变我原有的电路结构，并且给出的代码中要完整具体，不要省略。