2021/04/13 by Yuan Feng, Feng, Yuan, Raffaele Miceli +3
Computer Science · Physics and Astronomy · #Computability, Logic, AI Algorithms #FOS: Physical sciences #High Energy Physics - Theory (hep-th) #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Physics (quant-ph) #hep-th #quant-ph
paper · pdf · doi:10.48550/arxiv.2104.06458
openalex publication_date 2021/04/13 · openalex created_date 2021/04/26 · arxiv created 2021/06/21 · arxiv updated 2021/06/23 · openalex updated_date 2026/07/28
Topological data analysis is a rapidly developing area of data science where one tries to discover topological patterns in data sets to generate insight and knowledge discovery. In this project we use quantum walk algorithms to discover features of a data graph on which the walk takes place. This can be done faster on quantum computers where all paths can be explored using superposition. We begin with simple walks on a polygon and move up to graphs described by higher dimensional meshes. We use insight from the physics description of quantum walks defined in terms of probability amplitudes to go from one site on a graph to another distant site and show how this relates to the Feynman propagator or Kernel in the physics terminology. Our results from quantum computation using IBM's Qiskit quantum computing software were in good agreement with those obtained using classical computing methods.