Presentation
Quantum Spectral Clustering of Mixed Graphs
TimeTuesday, December 7th4:30pm - 4:50pm PST
Location3018
Event Type
Research Manuscript
Presented In-Person
Quantum Computing
Design
DescriptionIn this paper, we propose a quantum spectral clustering algorithm for discovering clusters and properties of mixed graphs. Our experimental results based on numerical simulations demonstrate that our quantum spectral clustering outperforms classical spectral clustering techniques. Specifically, our approach leads to a linear growth in complexity, while state-of-the-art classical counterpart leads to cubic growth. In a case study, we apply our proposed algorithm to preform unsupervised machine learning with real and simulated quantum computers. This work opens an avenue for efficient implementation of machine learning algorithms on mixed and directed graphs by making use of the inherent potential quantum speedup.