The aim of this study is to investigate the potential of employing ensemble learning to improve prediction of quantum text classification tasks. As a result, ...
This work highlights resource efficiency and reduced gate complexity and demonstrates the practical application of fidelity in quantum similarity calculations, ...
Feb 9, 2018 · Ensemble methods are based on the notion that allowing only one final model θ for prediction, whatever intricate the training procedure is, will ...
Nov 16, 2023 · In this work, we present an implementation and an extensive empirical evaluation of ensembles of quantum classifiers for binary classification, ...
This first high-level open-source Python toolkit for quantum natural language processing offers a fully automated quantum machine learning pipeline. Lambeq ...
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Nov 15, 2023 · In this work, we present an implementation and an extensive empirical evaluation of ensembles of quantum instance- based classifiers for binary ...
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Feb 2, 2024 · Another QML algorithm based on ensemble methods is Quantum Boosting [16]. This approach aims to enhance the performance of a weak learner by ...
This paper introduces quantum machine learning w.r.t text classification to readers of classical machine learning. It begins with a brief description of quantum ...
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Jan 10, 2024 · Here, we propose two ensemble-learning error mitigation methods, namely, bootstrap aggregating and adaptive boosting, which can significantly ...
Text classification research mainly focuses on feature representation of text documents or designing an efficient machine learning model. Although various ...