Not yet reviewed by Pith; the record is open.

Quadratic unconstrained binary optimization (QUBO) tasks are very important in chemistry, finance, job scheduling, and so on, which can be represented using graph structures, with the variables as nodes and the interaction between them as edges. Variational quantum algorithms, especially the Quantum Approximate Optimization Algorithm (QAOA) and its variants, present a promising way, potentially exceeding the capabilities of classical algorithms, for addressing QUBO tasks. However, the possibility of using message-passing machines, inspired by classical graph neural networks, to enhance the power and performance of these quantum algorithms for QUBO tasks was not investigated. This study introduces a novel variational quantum graph optimization algorithm that integrates the message-passing mechanism, which demonstrates significant improvements in performance for solving QUBO problems in terms of resource efficiency and solution precision, compared to QAOA, its variants, and other quantum graph neural networks. Furthermore, in terms of scalability on QUBO tasks, our algorithm shows superior performance compared to QAOA, presenting a substantial advancement in the field of quantum approximate optimization.