Efficient Quantum Algorithms for Data Clustering

Jury

• Giovanni ACAMPORA, Professor, Università degli Studi di Napoli Federico II (Rapporteur & Examinateur)
• Hachem KADRI, Professeur, Université Aix-Marseille (Rapporteur & Examinateur)
• Stéphane LOUISE, Directeur de recherche, CEA-LIST, Université Paris-Saclay (Examinateur)
• Simon MARTIEL, Docteur, IBM Enterprise (Examinateur)
• Patrick CARRIBAULT, Directeur de recherche, CEA-DAM, Université Paris-Saclay (Examinateur)

Abstract

Data clustering consists of grouping data without prior training, a process known as unsupervised learning. Given the constant growth of database volumes, contemporary computers require significant computational time to perform this partitioning.  

To effectively address this challenge, quantum computing offers a promising perspective, as it could eventually provide more efficient resources for clustering massive datasets. However, current quantum computers are not yet mature; they are prone to noise, which limits their practical utility.  

In this context, we have designed a hybrid classical-quantum clustering algorithm tailored for the current Noisy Intermediate-Scale Quantum (NISQ) era. We evaluated this algorithm under various conditions and across different types of datasets, comparing it against several well-known clustering algorithms. Our results demonstrate that, under certain conditions, quantum noise can actually provide an advantage to our algorithm. Furthermore, we worked on deploying this algorithm on actual quantum processors and developed tools to optimize this deployment. Finally, to diversify the quantum architectures used, we extended our research to include neutral-atom quantum computers.