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arXiv 2002.12790quant-phphysics.optics
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基于纠缠的量子深度学习

Entanglement-based quantum deep learning

Zhenwei Yang, Xiangdong Zhang

摘要:

经典的深度学习算法在学术界和工业界都引起了极大的兴趣,因为它们在图像识别、语言翻译、决策问题等方面都很有用。在这项工作中,我们提供了一个基于多量子比特纠缠态的量子深度学习方案,包括全量子过程中的神经网络计算和训练。在训练过程中,基于Greenberger-Horne-Zeilinger纠缠态,通过适当的测量,实现了未知单位矢量与已知单位矢量之间距离的有效计算。一个指数加速比经典算法已被证明。在计算过程中,给出了与多层前向神经网络相对应的量子方案。我们已经使用Iris数据集展示了我们方案的实用性。分析了该方案对不同类型模型的可扩展性

英文摘要:

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

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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