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

Recent work has proposed solving the k-means clustering problem on quantum
computers via the Quantum Approximate Optimization Algorithm (QAOA) and coreset
techniques. Although the current method demonstrates the possibility of quantum
k-means clustering, it does not ensure high accuracy and consistency across a
wide range of datasets. The existing coreset techniques are designed for
classical algorithms and there has been no quantum-tailored coreset technique
which is designed to boost the accuracy of quantum algorithms. In this work, we
propose solving the k-means clustering problem with the variational quantum
eigensolver (VQE) and a customised coreset method, the Contour coreset, which
has been formulated with specific focus on quantum algorithms. Extensive
simulations with synthetic and real-life data demonstrated that our VQE+Contour
Coreset approach outperforms existing QAOA+Coreset k-means clustering
approaches with higher accuracy and lower standard deviation. Our work has
shown that quantum tailored coreset techniques has the potential to
significantly boost the performance of quantum algorithms when compared to
using generic off-the-shelf coreset techniques.

我们提出使用变分量子本征求解器 (VQE) 和定制的 Contour 核心集方法来解决 k-means 聚类问题，与现有的 QAOA + 核心集 k-means 聚类方法相比，我们的 VQE + Contour 核心集方法在高准确性和较低标准差方面表现更好。