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Implementation of Quantum Support Vector Machine Algorithm Using a Benchmarking Dataset

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Title Implementation of Quantum Support Vector Machine Algorithm Using a Benchmarking Dataset
 
Creator Singh, Gurmohan
Kaur, Manjit
Singh, Mandeep
Kumar, Yadwinder
 
Subject Dirac notation
Hilbert space
Inner product
Machine Learning
Quantum bit
Principal Component Analysis
Support Vector Machine
 
Description 407-414
The evolution of quantum computers and quantum machine learning (QML) algorithms have started demonstrating
exponential speed-ups. In machine learning problems, the efficient handling and manipulation of linear algebra subroutines
defines the complexity of the task to be performed. Quantum computers handle big datasets in the form of vectors and matrix
operations very efficiently. In this paper, quantum support vector machine (QSVM) algorithm is used to solve a classification
problem using a benchmarking MNIST dataset of handwritten images of digits. Quantum SVM variational and kernel matrix
algorithms are implemented to analyze quantum speedup on quantum simulator and physical quantum processor back-ends. The
study compared classical and quantum SVM algorithms in terms of execution time and accuracy. The results explicitly prove
quantum speed-up achieved by quantum classifiers on quantum back-ends for machine learning applications.
 
Date 2022-05-13T09:16:24Z
2022-05-13T09:16:24Z
2022-05
 
Type Article
 
Identifier 0975-0959 (Online); 0301-1208 (Print)
http://nopr.niscair.res.in/handle/123456789/59710
 
Language en
 
Publisher CSIR-NIScPR, India
 
Source IJPAP Vol.60(05) [May 2022]