A high-efficiency variational quantum classifier for high-dimensional data
Document Type
Article
Publication Date
1-1-2025
Abstract
Variational quantum algorithms (VQAs) are most promising to show quantum advantages on noisy intermediate-scale quantum devices. Variational quantum classifiers (VQCs) are widely applied to classification tasks in the quantum domain. However, VQCs cannot show advantages in high-dimensional data. The large number of features necessitates the use of a significant number of qubits in VQCs. This results in long training time and increases training difficulty, ultimately leading to poor classification performance. In this paper, in order to enhance the ability of VQCs to handle high-dimensional data, a high-efficiency variational quantum classifier (HE-VQC) is proposed. Comparative Qiskit simulations of HE-VQC and four common VQCs were conducted on the UNSW-NB15 dataset. The simulation results show that HE-VQC significantly reduces training time while delivering superior classification performance.
Keywords
Quantum computing, Variational quantum algorithms, Variational quantum classifiers, Noisy intermediate-scale quantum
Divisions
fsktm
Funders
Liaoning Provincial Department of Education Research,Scientific Research Foundation for Advanced Talents from Shenyang Aerospace University (18YB06) ; (LJKZ0208)
Publication Title
Journal of Supercomputing
Volume
81
Issue
1
Publisher
Springer
Publisher Location
VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS