Variational Quantum Classifier for Binary Classification: Real vs Synthetic Dataset
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...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9665779/ |
| _version_ | 1798022168593825792 |
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| author | Danyal Maheshwari Daniel Sierra-Sosa Begonya Garcia-Zapirain |
| author_facet | Danyal Maheshwari Daniel Sierra-Sosa Begonya Garcia-Zapirain |
| author_sort | Danyal Maheshwari |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T17:25:28Z |
| format | Article |
| id | doaj.art-3ac5bc7696ed4c11979f54439d2b3315 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T17:25:28Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3ac5bc7696ed4c11979f54439d2b33152022-12-22T04:12:19ZengIEEEIEEE Access2169-35362022-01-01103705371510.1109/ACCESS.2021.31393239665779Variational Quantum Classifier for Binary Classification: Real vs Synthetic DatasetDanyal Maheshwari0https://orcid.org/0000-0001-7544-7817Daniel Sierra-Sosa1https://orcid.org/0000-0003-1326-0867Begonya Garcia-Zapirain2https://orcid.org/0000-0002-9356-1186EVIDA Research Group, Univeristy of Deusto, Bilbao, SpainComputer Science Department, Hood College, Frederick, MD, USAEVIDA Research Group, Univeristy of Deusto, Bilbao, SpainNowadays, 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.https://ieeexplore.ieee.org/document/9665779/Quantum machine learningstate preparationamplitude encodingvariational quantum classifier and T2DM diabetes |
| spellingShingle | Danyal Maheshwari Daniel Sierra-Sosa Begonya Garcia-Zapirain Variational Quantum Classifier for Binary Classification: Real vs Synthetic Dataset IEEE Access Quantum machine learning state preparation amplitude encoding variational quantum classifier and T2DM diabetes |
| title | Variational Quantum Classifier for Binary Classification: Real vs Synthetic Dataset |
| title_full | Variational Quantum Classifier for Binary Classification: Real vs Synthetic Dataset |
| title_fullStr | Variational Quantum Classifier for Binary Classification: Real vs Synthetic Dataset |
| title_full_unstemmed | Variational Quantum Classifier for Binary Classification: Real vs Synthetic Dataset |
| title_short | Variational Quantum Classifier for Binary Classification: Real vs Synthetic Dataset |
| title_sort | variational quantum classifier for binary classification real vs synthetic dataset |
| topic | Quantum machine learning state preparation amplitude encoding variational quantum classifier and T2DM diabetes |
| url | https://ieeexplore.ieee.org/document/9665779/ |
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