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Multiclass Classification of Hyperspectral Remote sensed Data using QSVC
Journal
Proceedings of SPIE - The International Society for Optical Engineering
ISSN
0277786X
Date Issued
2022-01-01
Author(s)
Pai, Archana G.
Buddhiraju, Krishna Mohan
Durbha, Surya S.
Abstract
Quantum Machine Learning (QML) is a branch of quantum computing that combines classical machine learning with the principles of quantum mechanics. It is emerging as an alternative to classical machine learning which exploits the quantum mechanical properties of entanglement and superposition to express the hidden patterns in the data. This reduces computational resources as well as the time required for processing. This study compares the overall performance of classical(SVC) and quantum(QSVC) Support Vector Classifiers for multiclass classification. In this study, we used benchmark Hyperspectral Remotely Sensed datasets namely, Pavia University and Salinas-A on IBM gate-based Quantum Computer(QC). In QSVC, kernel is generated by QC, and Qiskit’s Support Vector Classifier is used for classification. The classification of pixels into their respective classes was experimented using two techniques, One vs One (OVO) and One vs Rest (OVR). Quantum kernels are very expressive when compared to their classical counterparts and can learn complex data more efficiently. The overall accuracy of classification by QSVC is comparable to that of the classical SVC. We summarize our results by saying that QSVC performs better than SVC.
Volume
12262
Subjects