Work

Quantum Annealing in Machine Learning: QBoost on D-Wave Quantum Annealer

Year: 2024

Type: conference-paper

Abstract: Quantum computing (QC) has become a fascinating and popular topic due to its broad range of applications, particularly in machine learning (ML). The intersection of QC and ML is known as Quantum Machine Learning (QML). QML is a field that investigates how QC can enhance ML, making it one of the most exciting areas of research due to the potential of QC in solving complex problems. In this paper, we demonstrate the Quantum Annealing (QA) approach to improving ML in binary classification tasks. We implemented the QBoost algorithm on a few datasets using D-Wave’s quantum computers, specifically the Advantage 1 and Advantage 2 prototypes, incorporating the new feature Fast Anneal.

Source: Procedia Computer Science

DOI: 10.1016/j.procs.2024.09.311

Authors: Hadi Salloum, Ali Salloum, Manuel Mazzara, Sergey Zykov

Institutions: Innopolis University, National Research University Higher School of Economics

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W4404835150

FWCI: 1.222

Cites: 15

Cited by: 8

Related to: 10