Multiclass classification of hyperspectral remote sensed data using QSVC

Archana G. Pai; Krishna Mohan Buddhiraju; Surya S. Durbha

doi:10.1117/12.2648471

28 October 2022 Multiclass classification of hyperspectral remote sensed data using QSVC

Archana G. Pai, Krishna Mohan Buddhiraju, Surya S. Durbha

Proceedings Volume 12262, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIV; 122620P (2022) https://doi.org/10.1117/12.2648471
Event: SPIE Remote Sensing, 2022, Berlin, Germany

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 also the time required for processing. This research work is a comparative study, which compares the overall performance of Classical multi-class Support Vector Classifiers (SVC) with Quantum multi-class Support Vector Classifiers (QSVC). In this work, we used benchmark Hyperspectral Remotely Sensed datasets namely, Pavia University and Salinas-A on IBM gate-based Quantum Computer(QC). Here, in QSVC, kernel is generated by QC, and Qiskit’s Support Vector Classifier is used for classification. Classification of the pixels into their respective classes was experimented using two techniques, One vs One (OVO) and One v/s 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 research by saying that QSVC performs better than SVC.

Conference Presentation

Citation Download Citation

Archana G. Pai, Krishna Mohan Buddhiraju, and Surya S. Durbha "Multiclass classification of hyperspectral remote sensed data using QSVC", Proc. SPIE 12262, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIV, 122620P (28 October 2022); https://doi.org/10.1117/12.2648471

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