Can Entanglement-enhanced Quantum Kernels Improve Data Classification?

TL;DR

This paper investigates whether entanglement-enhanced quantum kernels can improve data classification accuracy, demonstrating significant improvements on complex datasets using quantum support vector machines compared to classical methods.

Contribution

The study introduces the use of entanglement-enhanced quantum kernels in QSVMs for complex data classification, showing improved accuracy over classical algorithms.

Findings

45% higher accuracy on complex respiratory datasets

Comparable performance on linear datasets

Effective utilization of quantum Hilbert space for complex data

Abstract

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.