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Quantum Computing for Efficient Learning in Prototype-based Vector Quantization

  • Prototype-based Vector Quantization is one of the key methods in data processing like data compression or interpretable classification learning. Prototype vectors serve as references for data and data classes. The data are given as vectors representing objects by numerical features. Famous approaches are the Neural Gas Vector Quantizer (NGVQ) for data compression and Learning Vector Quantizers (LVQ) for classification tasks. Frequently, training of those models is time consuming. In the contribution we discuss modifications of these algorithms adopting ideas from quantum computing. The aim for this is a least twofold: First quantum computing provides ideas for enormous speedup making use of quantum mechanical systems and inherent parallelization. Second, considering data and prototype vectors in terms of quantum systems, implicit data processing is performed, which frequently results in better data separation. We will highlight respective ideas and difficulties when equipping vector quantizers with quantum computing features.

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Metadaten
Author:Thomas Villmann, Alexander Engelsberger
URN:urn:nbn:de:bsz:mit1-opus4-122969
DOI:https://doi.org/10.48446/opus-12296
ISSN:1437-7624
Parent Title (German):26. Interdisziplinäre Wissenschaftliche Konferenz Mittweida
Publisher:Hochschule Mittweida
Place of publication:Mittweida
Document Type:Conference Proceeding
Language:English
Year of Completion:2021
Publishing Institution:Hochschule Mittweida
Contributing Corporation:Saxon Institute for Computational Intelligence and Machine Learning
Release Date:2021/05/18
Tag:Vector Quantization
GND Keyword:Maschinelles Lernen; Quantencomputer
Issue:002
Page Number:4
First Page:137
Last Page:140
Open Access:Frei zugänglich
Licence (German):License LogoUrheberrechtlich geschützt