Exploring the Potential of Quantum-Based Machine Learning: A Comparative Study of QSVM and Classical Machine Learning Algorithms - Ozpolat, Zeynep; Karabatak, Murat (Institute of Electrical and Electronics Engineers Inc., 2023)
Research using innovative, rapidly evolving quantum-based systems is becoming increasingly important. The acceleration provided by the real use of a few quantum computers in the world constitutes very important steps for the computer world. Quantum-based systems are also frequently used in data science and machine learning. In this study, Quantum Support Vector Machine (QSVM), a quantum-based machine learning algorithm, is compared with classical machine learning algorithms. IBM-Studies on quantum computer simulation have been carried out. The data dimension is reduced by first applying Principal Component Analysis (PCA) to the dataset. Afterwards, analyzes were made and the results were presented comparatively. The highest performance of the QSVM algorithm was obtained at 92.3%. Compared to classical machine learning algorithms, the performance of the QSVM algorithm is slightly lower. It is thought that higher performance and speed will be achieved in QSVM by eliminating limitations such as limited qubit resources and system restrictions. It is clear that the results found are promising for quantum-based machine learning algorithms. © 2023 IEEE.
11th International Symposium on Digital Forensics and Security, ISDFS 2023 -- 11 May 2023 through 12 May 2023 -- TN -- 189042
Keywords: classification; diabetes; principal component analysis; quantum machine learning; quantum support vector machine