论文标题
基于纠缠的量子深度学习
Entanglement-based quantum deep learning
论文作者
论文摘要
古典深度学习算法引起了对学术界和行业的极大兴趣，因为它们在图像识别，语言翻译，决策问题等方面的实用性等。在这项工作中，我们提供了一种基于多数纠缠状态的量子深度学习方案，包括在完整的量子过程中对神经网络的计算和培训。在训练过程中，根据Greenberger-Horne-Zeilinger纠缠状态，通过适当的测量来实现未知单位矢量和已知单位矢量之间距离的有效计算。已经证明了对经典算法的指数加速。在计算过程中，已经提供了与多层进发神经网络相对应的量子方案。我们已经使用IRIS数据集展示了我们计划的实用性。还已经分析了本方案对不同类型模型的可扩展性
Classical deep learning algorithms have aroused great interest in both academia and industry for their utility in image recognition, language translation, decision-making problems and more. In this work, we have provided a quantum deep learning scheme based on multi-qubit entanglement states, including computation and training of neural network in full quantum process. In the course of training, efficient calculation of the distance between unknown unit vector and known unit vector has been realized by proper measurement based on the Greenberger-Horne-Zeilinger entanglement states. An exponential speedup over classical algorithms has been demonstrated. In the process of computation, quantum scheme corresponding to multi-layer feedforward neural network has been provided. We have shown the utility of our scheme using Iris dataset. The extensibility of the present scheme to different types of model has also been analyzed
