Work

Quantum-ready vector quantization: Prototype learning as a binary optimization problem

Year: 2023

Type: conference-paper

Abstract: Quantum Computing Research proposed strategies to solve binary optimization problems.Application on current and near-term generation Hardware is possible.Even if computational benefits of the strategies are yet to be shown, we want to explore connections to prototype learning schemes.We examine cost functions for vector quantization based on data point selection and how they can be transformed into a common quadratic unconstrained binary optimization formulation (QUBO).There are different approaches for solving QUBO problems using quantum computer or quantum annealer hardware.We look at their current limits and how they might change.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2023.es2023-108

Authors: Alexander Engelsberger, Thomas Villmann

Institutions: Hochschule Mittweida, University of Groningen

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funder: Bundesministerium für Bildung und Forschung

OpenAlex ID: W4386815366

FWCI: 0.464

Cites: 17

Cited by: 4

Related to: 10