Can Entanglement-enhanced Quantum Kernels Improve Data Classification?

Classical machine learning, extensively utilized across diverse domains, faces limitations in speed, efficiency, parallelism, and processing of complex datasets. In contrast, quantum machine learning algorithms offer significant advantages, including exponentially faster computations, enhanced data...

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Bibliographic Details
Published in:arXiv.org 2024-06
Main Authors: Babu, Anand, Ghatnekar, Saurabh G, Saxena, Amit, Mandal, Dipankar
Format: Article
Language:English
Subjects:
Algorithms
Datasets
Hilbert space
Kernel functions
Machine learning
Quantum entanglement
Qubits (quantum computing)
Support vector machines
Online Access:Get full text
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Summary:Classical machine learning, extensively utilized across diverse domains, faces limitations in speed, efficiency, parallelism, and processing of complex datasets. In contrast, quantum machine learning algorithms offer significant advantages, including exponentially faster computations, enhanced data handling capabilities, inherent parallelism, and improved optimization for complex problems. In this study, we used the entanglement-enhanced quantum kernel in quantum support vector machine to train complex respiratory data sets. Compared to classical algorithms, our findings reveal that QSVM performs better with 45% higher accuracy for complex respiratory data sets while maintaining comparable performance with linear datasets in contrast to their classical counterparts executed on a 2-qubit system. Through our study, we investigate the efficacy of the QSVM-Kernel algorithm in harnessing the enhanced dimensionality of the quantum Hilbert space for effectively training complex datasets.
ISSN:2331-8422