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Quantum Computing Approaches for Vector Quantization-Current Perspectives and Developments.

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  • 1Saxon Institute for Computational Intelligence and Machine Learning (SICIM), University of Applied Sciences Mittweida, Technikumplatz 17, 09648 Mittweida, Germany.

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Summary
This summary is machine-generated.

This study explores quantum algorithms for vector quantization, a powerful machine learning technique. It reviews current quantum approaches for implementing these data representation methods on quantum devices.

Keywords:
prototype-based learningquantum machine learningvector quantization

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Area of Science:

  • Machine Learning
  • Quantum Computing
  • Data Representation

Background:

  • Vector quantization (VQ) offers low-complexity, interpretable methods for data representation, clustering, and classification.
  • The inherent simplicity of VQ makes it suitable for implementation on current and future quantum devices with restricted algorithmic capabilities.

Purpose of the Study:

  • To provide an overview of existing quantum algorithms and routines for realizing vector quantization concepts.
  • To assess the feasibility of applying quantum computing to VQ, considering adaptation and optimization paradigms.

Main Methods:

  • Literature review of quantum algorithms relevant to vector quantization.
  • Analysis of existing quantum routines for implementing VQ components on quantum devices.

Main Results:

  • Identification of current quantum approaches for partial or full implementation of VQ.
  • Assessment of the state-of-the-art in quantum computing for VQ.

Conclusions:

  • Quantum computing offers potential avenues for implementing vector quantization techniques.
  • Further research is needed to fully leverage quantum capabilities for advanced VQ applications.