Version 1
: Received: 5 November 2024 / Approved: 5 November 2024 / Online: 6 November 2024 (09:37:23 CET)
How to cite:
Ajibosin, S. S.; Cetinkaya, D. Implementation and Performance Evaluation of Quantum Machine Learning Algorithms for Binary Classification. Preprints2024, 2024110364. https://doi.org/10.20944/preprints202411.0364.v1
Ajibosin, S. S.; Cetinkaya, D. Implementation and Performance Evaluation of Quantum Machine Learning Algorithms for Binary Classification. Preprints 2024, 2024110364. https://doi.org/10.20944/preprints202411.0364.v1
Ajibosin, S. S.; Cetinkaya, D. Implementation and Performance Evaluation of Quantum Machine Learning Algorithms for Binary Classification. Preprints2024, 2024110364. https://doi.org/10.20944/preprints202411.0364.v1
APA Style
Ajibosin, S. S., & Cetinkaya, D. (2024). Implementation and Performance Evaluation of Quantum Machine Learning Algorithms for Binary Classification. Preprints. https://doi.org/10.20944/preprints202411.0364.v1
Chicago/Turabian Style
Ajibosin, S. S. and Deniz Cetinkaya. 2024 "Implementation and Performance Evaluation of Quantum Machine Learning Algorithms for Binary Classification" Preprints. https://doi.org/10.20944/preprints202411.0364.v1
Abstract
Quantum Machine Learning (QML) merges principles of Quantum Computing (QC) and Machine Learning (ML), offering improved efficiency and potential quantum advantage in data-driven tasks and solving complex problems. In binary classification, where the goal is to assign data into one of two categories, QML uses quantum algorithms to process large datasets efficiently. Quantum algorithms like Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN) exploit quantum parallelism and entanglement to enhance performance over classical methods. This research explores the use of QML algorithms for binary classification and compares their performance with classical ML methods. This study focuses on two common QML algorithms, Quantum Support Vector Classifier (QSVC) and QNN. We used the Qiskit software and did the experiments with three different datasets. Data preprocessing included dimensionality reduction using Principal Component Analysis (PCA) and standardization using scalers. The results showed that quantum algorithms demonstrated competitive performance against their classical counterparts in terms of accuracy, while QSVC performed better than QNN. These findings suggest that QML holds potential for improving computational efficiency in binary classification tasks. This opens the way for more efficient and scalable solutions in complex classification challenges and shows the complementary role of quantum computing.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
