In this paper, we present a performance comparison of machine learning algorithms executed on traditional and quantum computers. Quantum computing has potential of achieving incredible results for certain types of problems, and we explore if it can be applied to machine learning. First, we identified quantum machine learning algorithms with reproducible code and had classical machine learning counterparts. Then, we found relevant data sets with which we tested the comparable quantum and classical machine learning algorithm's performance. We evaluated performance with algorithm execution time and accuracy. We found that quantum variational support vector machines in some cases had higher accuracy than classical support vector machines on multi-class classification problems. The main conclusion was that quantum multi-class SVM classifiers have the potential to be useful in the future as quantum computer’s available number of qubits increases.
Havenstein, Christopher; Thomas, Damarcus; and Chandrasekaran, Swami
"Comparisons of Performance between Quantum and Classical Machine Learning,"
SMU Data Science Review: Vol. 1:
4, Article 11.
Available at: https://scholar.smu.edu/datasciencereview/vol1/iss4/11
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License