Qi, Han and Xiao, Sihui and Liu, Zhuo and Gong, Changqing and Gani, Abdullah (2025) A high-efficiency variational quantum classifier for high-dimensional data. Journal of Supercomputing, 81 (1). 154Liaoning Provincial Department of Education Research. ISSN 0920-8542, DOI https://doi.org/10.1007/s11227-024-06676-8.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Funders: | Liaoning Provincial Department of Education Research, Scientific Research Foundation for Advanced Talents from Shenyang Aerospace University (18YB06) ; (LJKZ0208) |
| Uncontrolled Keywords: | Quantum computing; Variational quantum algorithms; Variational quantum classifiers; Noisy intermediate-scale quantum |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 17 Mar 2025 05:03 |
| Last Modified: | 17 Mar 2025 05:03 |
| URI: | http://eprints.um.edu.my/id/eprint/47229 |
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