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Quantum Computing Approaches for Vector Quantization-Current Perspectives and Developments
Alexander Engelsberger1, Thomas Villmann1
1Saxon Institute for Computational Intelligence and Machine Learning (SICIM), University of Applied Sciences Mittweida, Technikumplatz 17, 09648 Mittweida, Germany.
Entropy (Basel, Switzerland)
|March 29, 2023
Summary
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.
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.
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