论文标题
QUCNN：基于纠缠的反向传播的量子卷积神经网络
QuCNN : A Quantum Convolutional Neural Network with Entanglement Based Backpropagation
论文作者
论文摘要
量子机学习仍然是量子计算中感兴趣的高度活跃领域。这些方法中的许多方法都采用了量子设置的经典方法，例如量子流等。我们推动了这一趋势，并证明了经典的卷积神经网络对量子系统的改编，即Qucnn。 qucnn是一个基于参数化的多量词 - 状态的神经网络层计算每个量子滤波器状态与每个量子数据状态之间的相似性。使用QUCNN，可以通过单轴量子量子常规实现返回传播。通过在MNIST图像的一小部分中应用卷积层和滤波器状态，对QUCNN进行验证，比较背部传播梯度，并训练过滤状态与理想目标状态进行训练。
Quantum Machine Learning continues to be a highly active area of interest within Quantum Computing. Many of these approaches have adapted classical approaches to the quantum settings, such as QuantumFlow, etc. We push forward this trend and demonstrate an adaption of the Classical Convolutional Neural Networks to quantum systems - namely QuCNN. QuCNN is a parameterised multi-quantum-state based neural network layer computing similarities between each quantum filter state and each quantum data state. With QuCNN, back propagation can be achieved through a single-ancilla qubit quantum routine. QuCNN is validated by applying a convolutional layer with a data state and a filter state over a small subset of MNIST images, comparing the back propagated gradients, and training a filter state against an ideal target state.
