quantum_ml

# 使用 PennyLane 进行量子机器学习 ## 目录 1. [混合量子-经典模型](#hybrid-quantum-classical-models) 2. [框架集成](#framework-integration) 3. [量子神经网络](#quantum-neural-networks) 4. [变分分类器](#variational-classifiers) 5. [训练与优化](#training-and-optimization) 6. [数据编码策略](#data-encoding-strategies) 7. [迁移学习](#transfer-learning) ## 混合量子-经典模型 ### 基础混合模型 python import pennylane as qml import numpy as np dev = qml.device('default.qubit', wires=4) @qml.qnode(dev) def quantum_layer(inputs, weights): # 编码经典数据 for i, inp in enumerate(inputs): qml.RY(inp, wires=i) # 参数化量子电路 for wire in range(4): qml.RX(weights[wire], wires=wire) for wire in range(3): qml.CNOT(wires=[wire, wire+1]) # 测量 return [qml.expval(qml.PauliZ(i)) for i in range(4)] # 在经典工作流中使用 inputs = np.array([0.1, 0.2, 0.3, 0.4]) weights = np.random.random(4) output = quantum_layer(inputs, weights) ### 量子-经典流水线 python def hybrid_model(x, quantum_weights, classical_weights): # 经典预处理 x_preprocessed = np.tanh(classical_weights['pre'] @ x) # 量子层 quantum_out = quantum_layer(x_preprocessed, quantum_weights) # 经典后处理 output = classical_weights['post'] @ quantum_out return output ## 框架集成 ### PyTorch 集成 python import torch import pennylane as qml dev = qml.device('default.qubit', wires=2) @qml.qnode(dev, interface='torch') def quantum_circuit(inputs, weights): qml.RY(inputs[0], wires=0) qml.RY(inputs[1], wires=1) qml.RX(weights[0], wires=0) qml.RX(weights[1], wires=1) qml.CNOT(wires=[0, 1]) return qml.expval(qml.PauliZ(0)) # 创建 PyTorch 层 class QuantumLayer(torch.nn.Module): def __init__(self, n_qubits): super().__init__() self.n_qubits = n_qubits self.weights = torch.nn.Parameter(torch.randn(n_qubits)) def forward(self, x): return torch.stack([quantum_circuit(xi, self.weights) for xi in x]) # 在 PyTorch 模型中使用 class HybridModel(torch.nn.Module): def __init__(self): super().__init__() self.classical_1 = torch.nn.Linear(10, 2) self.quantum = QuantumLayer(2) self.classical_2 = torch.nn.Linea