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- W4285146939 abstract "AbstractCorrelation is an important information resource, which is often used as a fundamental quantity for modeling tasks in machine learning. Since correlation between quantum entangled systems often surpasses that between classical systems, quantum information processing methods show superiority that classical methods do not possess. In this paper, we study the virtue of entangled systems and propose a novel classification algorithm called Quantum Entanglement inspired the Classification Algorithm (QECA). Particularly, we use the joint probability derived from entangled systems to model correlation between features and categories, that is, Quantum Correlation (QC), and leverage it to develop a novel QC-induced Multi-layer Perceptron framework for classification tasks. Experimental results on four datasets from diverse domains show that QECA is significantly better than the baseline methods, which demonstrates that QC revealed by entangled systems can improve the classification performance of traditional algorithms.KeywordsQuantum correlationQuantum-inspired algorithmsClassification algorithm" @default.
- W4285146939 created "2022-07-14" @default.
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- W4285146939 date "2022-01-01" @default.
- W4285146939 modified "2023-09-30" @default.
- W4285146939 title "Quantum Entanglement Inspired Correlation Learning for Classification" @default.
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- W4285146939 doi "https://doi.org/10.1007/978-3-031-05936-0_5" @default.
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