A PREPROCESSING PERSPECTIVE FOR QUANTUM MACHINE LEARNING CLASSIFICATION ADVANTAGE USING NISQ ALGORITHMS

作     者：Mancilla, Javier Pere, Christophe 

作者机构：Stafford Computing LLC 16192 Coastal Highway LewesDEDE 19958 United States INTRIQ Department of Computer Science and Software Engineering Université Laval Québec Canada 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2022年

核心收录：

主　　题：Machine learning 

摘      要：Quantum Machine Learning (QML) hasn’t yet demonstrated extensively and clearly its advantages compared to the classical machine learning approach. So far, there are only specific cases where some quantum-inspired techniques have achieved small incremental advantages, and a few experimental cases in hybrid quantum computing are promising considering a mid-term future (not taking into account the achievements purely associated with optimization using quantum-classical algorithms). The current quantum computers are noisy and have few qubits to test, making it difficult to demonstrate the current and potential quantum advantage of QML methods. This study shows that we can achieve better classical encoding and performance of quantum classifiers by using Linear Discriminant Analysis (LDA) during the data preprocessing step. As a result, Variational Quantum Algorithm (VQA) shows a gain of performance in balanced accuracy with the LDA technique and outperforms baseline classical classifiers. © 2022, CC BY.