从量子图计算到量子图学习：综述
From Quantum Graph Computing to Quantum Graph Learning: A Survey
摘要：
量子计算（QC）是一种新的计算范式，其基础与量子物理有关。已经取得了显著的进展，推动了一系列利用量子计算能力的基于量子的算法的诞生。在本文中，我们提供了一个有针对性的调查的发展，质量控制的图形相关的任务。我们首先阐述了量子力学和图论之间的关系，以表明量子计算机能够产生有用的解决方案，不能有效地产生经典系统的一些问题的图。基于其实用性和广泛的适用性，我们简要回顾了为各种任务设计的典型图学习技术。受这些强大的方法的启发，我们注意到，先进的量子算法已被提出用于表征图结构。我们给出了量子图学习的快照，其中期望作为后续研究的催化剂。我们进一步讨论了在图学习中使用量子算法的挑战，以及更灵活和通用的量子图学习求解器的未来方向。
英文摘要：
Quantum computing (QC) is a new computational paradigm whose foundations relate to quantum physics. Notable progress has been made, driving the birth of a series of quantum-based algorithms that take advantage of quantum computational power. In this paper, we provide a targeted survey of the development of QC for graph-related tasks. We first elaborate the correlations between quantum mechanics and graph theory to show that quantum computers are able to generate useful solutions that can not be produced by classical systems efficiently for some problems related to graphs. For its practicability and wide-applicability, we give a brief review of typical graph learning techniques designed for various tasks. Inspired by these powerful methods, we note that advanced quantum algorithms have been proposed for characterizing the graph structures. We give a snapshot of quantum graph learning where expectations serve as a catalyst for subsequent research. We further discuss the challenges of using quantum algorithms in graph learning, and future directions towards more flexible and versatile quantum graph learning solvers.
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