图结构数据的量子机器学习
Quantum machine learning of graph-structured data
摘要：
图结构在自然科学中无处不在。在这里，我们考虑图结构的量子数据，并描述如何通过量子神经网络进行量子机器学习。特别地，我们考虑以与图的顶点相关联的输入和输出量子态对的形式的训练数据，以及对顶点之间的相关性进行编码的边。我们解释了如何系统地利用这种额外的图结构来改进量子学习算法。这些算法进行了数值模拟，并表现出良好的学习行为。学习过程的可扩展量子实现在下一代量子计算设备上可能是可行的。
英文摘要：
Graph structures are ubiquitous throughout the natural sciences. Here we consider graph-structured quantum data and describe how to carry out its quantum machine learning via quantum neural networks. In particular, we consider training data in the form of pairs of input and output quantum states associated with the vertices of a graph, together with edges encoding correlations between the vertices. We explain how to systematically exploit this additional graph structure to improve quantum learning algorithms. These algorithms are numerically simulated and exhibit excellent learning behavior. Scalable quantum implementations of the learning procedures are likely feasible on the next generation of quantum computing devices.
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