In recent years, quantum computing has emerged as a transformative force in
the field of combinatorial optimization, offering novel approaches to tackling
complex problems that have long challenged classical computational methods.
Among these, the Quantum Approximate Optimization Algorithm (QAOA) stands out
for its potential to efficiently solve the Max-Cut problem, a quintessential
example of combinatorial optimization. However, practical application faces
challenges due to current limitations on quantum computational resource. Our
work optimizes QAOA initialization, using Graph Neural Networks (GNN) as a
warm-start technique. This sacrifices affordable computational resource on
classical computer to reduce quantum computational resource overhead, enhancing
QAOA's effectiveness. Experiments with various GNN architectures demonstrate
the adaptability and stability of our framework, highlighting the synergy
between quantum algorithms and machine learning. Our findings show GNN's
potential in improving QAOA performance, opening new avenues for hybrid
quantum-classical approaches in quantum computing and contributing to practical
applications.

我们的研究通过使用图神经网络（GNN）作为热启动技术来优化 QAOA 初始化，为了减少量子计算资源的开销，牺牲了大量的经济计算资源，从而提高了 QAOA 的效果；实验结果表明 GNN 有助于提高 QAOA 的性能，为量子计算中的混合量子 - 经典方法开辟了新的途径，也为实际应用做出了贡献。

图学习用于量子近似优化算法参数预测

Graph Learning for Parameter Prediction of Quantum Approximate  Optimization Algorithm

Graph-based fraud detection (GFD) can be regarded as a challenging
semi-supervised node binary classification task. In recent years, Graph Neural
Networks(GNN) have been widely applied to GFD, characterizing the anomalous
possibility of a node by aggregating neighbor information. However, fraud
graphs are inherently heterophilic, thus most of GNNs perform poorly due to
their assumption of homophily. In addition, due to the existence of heterophily
and class imbalance problem, the existing models do not fully utilize the
precious node label information. To address the above issues, this paper
proposes a semi-supervised GNN-based fraud detector SEC-GFD. This detector
includes a hybrid filtering module and a local environmental constraint module,
the two modules are utilized to solve heterophily and label utilization problem
respectively. The first module starts from the perspective of the spectral
domain, and solves the heterophily problem to a certain extent. Specifically,
it divides the spectrum into multiple mixed frequency bands according to the
correlation between spectrum energy distribution and heterophily. Then in order
to make full use of the node label information, a local environmental
constraint module is adaptively designed. The comprehensive experimental
results on four real-world fraud detection datasets show that SEC-GFD
outperforms other competitive graph-based fraud detectors.

本研究提出了一种半监督的基于图神经网络的欺诈检测器 SEC-GFD，通过混合过滤模块和局部环境约束模块解决了异态性和标签利用问题，实验证明 SEC-GFD 在四个真实世界的欺诈检测数据集上优于其他竞争对手的基于图的欺诈检测器。