论文标题
从量子图计算到量子图学习：调查
From Quantum Graph Computing to Quantum Graph Learning: A Survey
论文作者
论文摘要
量子计算（QC）是一种新的计算范式，其基础与量子物理有关。已经取得了显着的进步，推动了利用量子计算能力的一系列基于量子的算法的诞生。在本文中，我们提供了针对图形相关任务的QC开发的针对性调查。我们首先详细阐述了量子力学和图形论之间的相关性，以表明量子计算机能够生成有用的解决方案，而这些解决方案无法有效地用于与图形相关的某些问题。为了实用性和广泛的可实现性，我们简要审查了为各种任务设计的典型图形学习技术。受这些强大方法的启发，我们注意到已经提出了高级量子算法来表征图形结构。我们提供了量子图学习的快照，期望是随后研究的催化剂。我们进一步讨论了在图形学习中使用量子算法的挑战，以及将来朝着更灵活和多功能的量子图学习求解器的方向。
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.
