A Comparative Analysis of Quantum-inspired Algorithms for Optimizing Machine Learning Models

Kim Phillips; Kim Thompson; Henry Brown; Jordan Lopez

Authors

Kim Phillips PhD
Kim Thompson Professor
Henry Brown Associate Professor
Jordan Lopez Dr. Sc

Keywords:

quantum-inspired algorithms, machine learning optimization, variational quantum algorithms, computational efficiency, high-dimensional datasets

Abstract

This article investigates the efficacy of quantum-inspired algorithms in optimizing machine learning models. By exploring variational quantum algorithms and their classical counterparts, we identify performance metrics and computational efficiency. Our results demonstrate that quantum-inspired approaches can offer significant improvements in optimization, particularly in high-dimensional datasets. This warrants further investigation into their practical applications across various domains.

Author Biographies

Kim Phillips, PhD

PhD
Technical University of Munich
Arcisstrasse 21, 80333 Munich, Germany

Kim Thompson, Professor

Professor
Massachusetts Institute of Technology
77 Massachusetts Avenue, Cambridge, MA 02139, USA

Henry Brown, Associate Professor

Associate Professor
University of Cambridge
The Old Schools, Trinity Ln, Cambridge CB2 1TN, United Kingdom

Jordan Lopez, Dr. Sc

Dr. Sc
Australian National University
East Rd, Acton ACT 2601, Australia

References

Kumar, N., & Kataria, V. Enhanced Sentiment Classification using a Multi-layered Stacked Ensemble Architecture.

KUMAR, Nitin; KATARIA, Vipin. Enhanced Sentiment Classification using a Multi-layered Stacked Ensemble Architecture.

Искендерзаде, Э. Б. О., Рагимов, Э. Р. О., & Джейхун, Р. (2024). НОВЫЕ КРИТЕРИИ ОЦЕНКИ ЭМИССИИ С И СО2 В ВОЗДУХ АВТОМОБИЛЬНЫМ ТРАНСПОРТОМ. Природные системы и ресурсы, 14(2), 47-54.

Stall, K., & Kruk, G. (2023). The impact of technological innovation on employee retention in enterprises: a case study of quality control. Zarządzanie Innowacyjne w Gospodarce i Biznesie, 37(2), 57-77.