Quantum density peak clustering

Abstract Clustering algorithms are of fundamental importance when dealing with large unstructured datasets and discovering new patterns correlations therein, applications ranging from scientific research to medical imaging marketing analysis. In this work, we introduce a quantum version the density peak clustering algorithm, built upon routine for minimum finding. We prove speedup decision depending on structure dataset. Specifically, is dependent heights trees induced graph nearest-highers, i.e. connections nearest elements higher density. discuss condition, showing that our algorithm particularly suitable high-dimensional datasets. Finally, benchmark proposal toy problem real device.