Experimental Evaluation of Quantum Machine Learning Algorithms

Machine learning and quantum computing are both areas with considerable progress in recent years. The combination of these disciplines holds great promise for both research and practical applications. Recently there have also been many theoretical contributions of quantum machine learning algorithms...

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Bibliographic Details
Published in:IEEE access 2023-01, Vol.11, p.1-1
Main Authors: Simoes, Ricardo Daniel Monteiro, Huber, Patrick, Meier, Nicola, Smailov, Nikita, Fuchslin, Rudolf M., Stockinger, Kurt
Format: Article
Language:English
Subjects:
Algorithms
Computer simulation
Computers
Datasets
experimental evaluation
Feature maps
Kernel functions
Machine learning
Neural networks
Performance evaluation
Quantum computers
Quantum computing
Quantum mechanics
Qubit
Simulators
Support vector machines
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Summary:Machine learning and quantum computing are both areas with considerable progress in recent years. The combination of these disciplines holds great promise for both research and practical applications. Recently there have also been many theoretical contributions of quantum machine learning algorithms with experiments performed on quantum simulators. However, most questions concerning the potential of machine learning on quantum computers are still unanswered such as How well do current quantum machine learning algorithms work in practice? How do they compare with classical approaches? Moreover, most experiments use different datasets and hence it is currently not possible to systematically compare different approaches. In this paper we analyze how quantum machine learning can be used for solving small, yet practical problems. In particular, we perform an experimental analysis of kernel-based quantum support vector machines and quantum neural networks. We evaluate these algorithm on 5 different datasets using different combinations of quantum feature maps. Our experimental results show that quantum support vector machines outperform their classical counterparts on average by 3 to 4% in accuracy both on a quantum simulator as well as on a real quantum computer. Moreover, quantum neural networks executed on a quantum computer further outperform quantum support vector machines on average by up to 5% and classical neural networks by 7%.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3236409