Quantum density peak clustering

Abstract

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

References in corpus (12)

• Quantum Machine Learning
• Quantum Mechanics helps in searching for a needle in a haystack
• Quantum principal component analysis
• Quantum random access memory
• Tight bounds on quantum searching
• The quest for a Quantum Neural Network
• Quantum Data Fitting
• Architectures for a quantum random access memory
• Complete 3-Qubit Grover Search on a Programmable Quantum Computer
• Circuit-Based Quantum Random Access Memory for Classical Data
• Fault tolerant resource estimation of quantum random-access memories
• Quantum Spectral Clustering