Experimental Comparison of Quantum and Classical Support Vector Machines
Balika J. Chelliah1, ShristiShreyasi2, Ananya Pandey3, Kirti Singh4
1Dr. Balika.J.Chelliah M.Tech, Ph.D. Associate Professor, Department of CSE, SRMIST, Ramapuram, Chennai (Tamil Nadu), India.
2Shristi Shreyasi, UG Scholar, Department of CSE Department, SRMIST, Ramapuram, Chennai (Tamil Nadu), India.
3Ananya Pandey, UG Scholar, Department of CSE Department, SRMIST, Ramapuram, Chennai (Tamil Nadu), India.
4Kirti Singh, UG Scholar, Department of CSE Department, SRMIST, Ramapuram, Chennai (Tamil Nadu), India.
Manuscript received on 07 April 2019 | Revised Manuscript received on 20 April 2019 | Manuscript published on 30 April 2019 | PP: 208-211 | Volume-8 Issue-6, April 2019 | Retrieval Number: F3441048619/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Classical Support Vector Machine is hugely popular for classifying data efficiently whether it is linear or non-linear in nature. SVM has been used immensely to assist a precise classification of a data point. The kernel trick of SVM has also elevated the performance of the classical algorithm. But, SVM suffers a lot of problems on a classical machine when higher dimensions are introduced or large datasets are taken up. So, in order to enhance the efficiency of Support Vector Machine, the idea of running it on a quantum machine takes over.A Quantum Machine uses Qubits which is a single bit representing 0, 1 and superposition states of 0 & 1. This use of Qubit introduces the concept of ‘parallel processing’. The Quantum Machine utilises a different version of the SVM algorithm for performing the task of classification. In the algorithm, classical data is transformed into quantum data and then analysed over a Quantum Machine. For this experiment, the outcomes from both Classical Machine as well as Quantum Machine will be compared to determine Quantum Machine’s precedence over Classical Machine is justified or not. The comparison parameters are execution time and accuracy percentile of both the approaches. These results will be compared for asserting the importance of Quantum Approach in increasing machine learning’s scope, application and potential for current, future and deemed impossible task.
Keyword: Quantum Machine, Quantum Machine Learning, Quantum Support Vector Machine, Support Vector Machine.
Scope of the Article: Machine Learning