TY - GEN
T1 - Performance Analysis of Quantum-Enhanced Kernel Classifiers Based on Feature Maps
T2 - 31st International Conference on Neural Information Processing, ICONIP 2024
AU - Jha, Ravi Kumar
AU - Kasabov, Nikola
AU - Coyle, Damien
AU - Bhattacharyya, Saugat
AU - Prasad, Girijesh
PY - 2024/12/6
Y1 - 2024/12/6
N2 - Quantum machine learning (QML) has gained significant attention recently for exploring the quantum computing applications. In this work, we delve into QML applications using the quantum support vector classifier (QSVC). QSVC uses quantum feature maps to create quantum-enhanced kernels. These kernels have the potential to provide a quantum advantage over classical machine learning (ML) algorithms in data classification. In this study, we introduced a novel quantum feature map and analyzed its role in developing a quantum-enhanced classifier, thereby expanding the applications of QML. Additionally, the study explored the importance of hyperparameter tuning for achieving quantum enhancement. To demonstrate proof of concept, we analyzed motor imagery electroencephalogram (EEG) datasets using the quantum kernel. We compared the results from our novel quantum feature map with outcomes from state-of-the-art feature maps. Additionally, we assessed the performance of quantum kernels in comparison with classical kernels and other ML classifiers. Finally, the study demonstrates the analytical advantages of the proposed quantum feature map over state-of-the-art feature maps and various classical classifiers through classification accuracy.
AB - Quantum machine learning (QML) has gained significant attention recently for exploring the quantum computing applications. In this work, we delve into QML applications using the quantum support vector classifier (QSVC). QSVC uses quantum feature maps to create quantum-enhanced kernels. These kernels have the potential to provide a quantum advantage over classical machine learning (ML) algorithms in data classification. In this study, we introduced a novel quantum feature map and analyzed its role in developing a quantum-enhanced classifier, thereby expanding the applications of QML. Additionally, the study explored the importance of hyperparameter tuning for achieving quantum enhancement. To demonstrate proof of concept, we analyzed motor imagery electroencephalogram (EEG) datasets using the quantum kernel. We compared the results from our novel quantum feature map with outcomes from state-of-the-art feature maps. Additionally, we assessed the performance of quantum kernels in comparison with classical kernels and other ML classifiers. Finally, the study demonstrates the analytical advantages of the proposed quantum feature map over state-of-the-art feature maps and various classical classifiers through classification accuracy.
KW - Classification
KW - EEG
KW - Feature Map
KW - Quantum Kernel
KW - Quantum Machine Learning
UR - https://www.scopus.com/pages/publications/105010137713
U2 - 10.1007/978-981-96-6579-2_25
DO - 10.1007/978-981-96-6579-2_25
M3 - Chapter in a published conference proceeding
AN - SCOPUS:105010137713
SN - 9789819665785
T3 - Lecture Notes in Computer Science
SP - 371
EP - 383
BT - Neural Information Processing - 31st International Conference, ICONIP 2024, Proceedings
A2 - Mahmud, Mufti
A2 - Doborjeh, Maryam
A2 - Wong, Kevin
A2 - Leung, Andrew Chi Sing
A2 - Doborjeh, Zohreh
A2 - Tanveer, M.
PB - Springer
CY - Singapore
Y2 - 2 December 2024 through 6 December 2024
ER -