This work presents a hybrid quantum-classical algorithm to perform clustering aggregation, designed for neutral-atoms quantum computers and quantum annealers. Clustering aggregation is a technique that mitigates the weaknesses of clustering algorithms, an important class of data science methods for partitioning datasets, and is widely employed in many real-world applications. By expressing the clustering aggregation problem instances as a Maximum Independent Set (MIS) problem and as a Quadratic Unconstrained Binary Optimization (QUBO) problem, it was possible to solve them by leveraging the potential of Pasqal's Fresnel (neutral-atoms processor) and D-Wave's Advantage QPU (quantum annealer). Additionally, the designed clustering aggregation algorithm was first validated on a Fresnel emulator based on QuTiP and later on an emulator of the same machine based on tensor networks, provided by Pasqal. The results revealed technical limitations, such as the difficulty of adding additional constraints on the employed neutral-atoms platform and the need for better metrics to measure the quality of the produced clusterings. However, this work represents a step towards a benchmark to compare two different machines: a quantum annealer and a neutral-atom quantum computer. Moreover, findings suggest promising potential for future advancements in hybrid quantum-classical pipelines, although further improvements are needed in both quantum and classical components.

翻译：本研究提出了一种混合量子-经典算法，用于执行聚类聚合任务，该算法专为中性原子量子计算机和量子退火器设计。聚类聚合是一种能够弥补聚类算法弱点的技术，聚类算法作为数据科学中用于划分数据集的重要方法类别，在众多实际应用中广泛使用。通过将聚类聚合问题实例表达为最大独立集问题与二次无约束二元优化问题，我们得以利用Pasqal公司的Fresnel中性原子处理器和D-Wave公司的Advantage量子处理器的潜力进行求解。此外，所设计的聚类聚合算法首先在基于QuTiP的Fresnel模拟器上进行了验证，随后在Pasqal提供的基于张量网络的同型机器模拟器上进行了测试。结果揭示了若干技术局限性，例如在中性原子平台上添加额外约束的困难性，以及需要建立更优的指标来衡量所生成聚类结果的质量。尽管如此，本研究为建立比较两种不同量子计算平台——量子退火器与中性原子量子计算机——的基准测试迈出了重要一步。研究结果表明混合量子-经典计算流程在未来发展中具有广阔前景，但量子与经典计算组件仍需进一步改进。