Variational quantum classifier forbBinary classification: real vs synthetic dataset
| dc.contributor.author | Maheshwari, Danyal | |
| dc.contributor.author | Sierra-Sosa, Daniel | |
| dc.contributor.author | García-Zapirain, Begoña | |
| dc.date.accessioned | 2024-11-14T11:37:32Z | |
| dc.date.available | 2024-11-14T11:37:32Z | |
| dc.date.issued | 2022 | |
| dc.date.updated | 2024-11-14T11:37:32Z | |
| dc.description.abstract | Nowadays, quantum-enhanced methods have been widely studied to solve machine learning related problems. This article presents the application of a Variational Quantum Classifier (VQC) for binary classification. We utilized three datasets: a synthetic dataset with randomly generated values between 0 and 1, the publicly available University of California Intelligence Machine learning (UCI) sonar dataset consisting of mining data, and a proprietary diabetes dataset related to diabetes with acute diseases and diabetes without acute disease. To deal with the limitation of noisy intermediate-scale quantum systems (NISQ), we used a pre-processing method to enhance the prediction rate when applying the VQC method. The process includes feature selection and state preparation. Quantum state preparation is critical for obtaining a functioning pipeline in a quantum machine learning (QML) model. Amplitude encoding is a state preparation approach that enhances the performance of data encoding and the learning of quantum models. As a result, our proposed methods achieved accuracies of 75%, 71.4%, and 68.73% by using VQC model and in contrast, the amplitude encoding-based VQC achieved 98.40%, 67.3%, and 74.50% accuracies on the synthetic, sonar, and diabetes dataset, respectively. | en |
| dc.description.sponsorship | This work was supported by the eVIDA Research Group, University of Deusto, Bilbao, Spain, through the Basque Government's under Grant IT 905-16 | en |
| dc.identifier.citation | Maheshwari, D., Sierra-Sosa, D., & Garcia-Zapirain, B. (2022). Variational Quantum Classifier for Binary Classification: Real vs Synthetic Dataset. IEEE Access, 10, 3705-3715. https://doi.org/10.1109/ACCESS.2021.3139323 | |
| dc.identifier.doi | 10.1109/ACCESS.2021.3139323 | |
| dc.identifier.issn | 2169-3536 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.14454/1860 | |
| dc.language.iso | eng | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.subject.other | Amplitude encoding | |
| dc.subject.other | Quantum machine learning | |
| dc.subject.other | State preparation | |
| dc.subject.other | Variational quantum classifier and T2DM diabetes | |
| dc.title | Variational quantum classifier forbBinary classification: real vs synthetic dataset | en |
| dc.type | journal article | |
| dcterms.accessRights | open access | |
| oaire.citation.endPage | 3715 | |
| oaire.citation.startPage | 3705 | |
| oaire.citation.title | IEEE Access | |
| oaire.citation.volume | 10 | |
| oaire.licenseCondition | https://creativecommons.org/licenses/by/4.0/ | |
| oaire.version | VoR |
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