Quantum graph learning and algorithms applied in quantum computer sciences and image classification

Graph and network theory play a fundamental role in quantum computer sciences, including quantum information and computation. Random graphs and complex network theory are pivotal in predicting novel quantum phenomena, where entangled links are represented by edges. Quantum algorithms have been developed to enhance solutions for various network problems, giving rise to quantum graph computing and quantum graph learning (QGL). Here, in this review, we explore graph theory and graph learning methods as powerful tools for quantum computers to generate efficient solutions to problems beyond the reach of classical systems. We delve into the development of quantum complex network theory and its applications in quantum computation, materials discovery, and research. We also discuss quantum machine learning (QML) methodologies for effective image classification using qubits, quantum gates, and quantum circuits. Additionally, the paper addresses the challenges of QGL and algorithms, emphasizing the steps needed to develop flexible QGL solvers. This review presents a comprehensive overview of the fields of QGL and QML, highlights recent advancements, and identifies opportunities for future research.. Authors: Shayeganfar, Farzaneh [University of Michigan, Ann Arbor, MI (United States); Amirkabir University of Technology, Tehran (Iran)] (ORCID:0000000181875156); Ramazani, Ali [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)] (ORCID:0000000268871086); Sundararaghavan, Veera [University of Michigan, Ann Arbor, MI (United States)] (ORCID:0000000212137958); Duan, Yuhua [National Energy Technology Laboratory (NETL), Pittsburgh, PA (United States)] (ORCID:0000000174470142). Subjects: 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; image classification; materials search; quantum circuit; quantum graph learning; quantum graph neural networks; quantum information science; quantum machine learning