通过量子增强型 AI 区块链重新定义分布式智能

    from AITchain import QuantumBridge, AIModel
    
    # 初始化量子桥接
    quantum_bridge = QuantumBridge(
        qubits=100,
        error_mitigation=True,
        optimization_level="high"
    )
    
    # 加载AI模型
    model = AIModel.load("transformer_large")
    
    # 定义量子增强训练函数
    def quantum_enhanced_training(model, training_data, epochs):
        # 准备经典特征
        classical_features = model.extract_features(training_data)
        
        # 量子降维
        quantum_features = quantum_bridge.reduce_dimensions(
            features=classical_features,
            target_dimensions=64,
            algorithm="quantum_pca"
        )
        
        # 使用量子随机权重初始化模型
        initial_weights = quantum_bridge.generate_random_state(
            dimensions=model.parameter_count,
            distribution="gaussian"
        )
        model.set_weights(initial_weights)
        
        # 训练循环与量子优化
        for epoch in range(epochs):
            # 经典前向传播
            predictions = model.forward(quantum_features)
            loss = model.calculate_loss(predictions, training_data.labels)
            
            # 量子梯度优化
            gradients = model.calculate_gradients(loss)
            optimized_gradients = quantum_bridge.optimize_gradients(
                gradients=gradients,
                algorithm="quantum_adam",
                learning_rate=0.001
            )
            
            # 更新模型权重
            model.update_weights(optimized_gradients)
            
            print(f"Epoch {epoch}, Loss: {loss}")
        
        return model