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RETRACTED ARTICLE: Quantum K-means clustering method for detecting heart disease using quantum circuit approach.
S S Kavitha1, Narasimha Kaulgud1
1Electronics and Communication Engineering, The National Institute of Engineering, Manandavadi Road, Mysuru, Karnataka 570008 India.
Soft Computing
|June 7, 2022
Summary
This study explores quantum computing to accelerate unsupervised machine learning, specifically K-means clustering. A novel quantum circuit enhances distance calculations, offering potential speedups over classical methods for data analysis.
Area of Science:
- Quantum Computing
- Machine Learning
- Data Mining
Background:
- Noisy Intermediate-Scale Quantum (NISQ) computers show promise for quantum advantage.
- Unsupervised machine learning algorithms can benefit from computational speedups.
- K-means clustering is a fundamental data mining technique.
Purpose of the Study:
- To investigate the application of quantum computing paradigms for accelerating unsupervised machine learning.
- To specifically enhance the K-means clustering algorithm using quantum computation.
- To evaluate the performance of a quantum-enhanced K-means approach against its classical counterpart.
Main Methods:
- Development of a quantum circuit to perform distance calculations essential for K-means clustering.
- Integration of data mining techniques with quantum computation principles.
- Preprocessing of a heart disease dataset for clustering analysis.
Main Results:
- Evaluation of classical K-means clustering performance on the dataset.
- Application of the quantum circuit approach to the clustering algorithm.
- Comparative analysis of performance metrics between quantum and classical K-means processing.
Conclusions:
- Quantum computing offers a potential pathway to speed up unsupervised machine learning tasks.
- The proposed quantum circuit for distance calculation is a key component in enhancing K-means.
- Further research and development are needed to fully realize the benefits of quantum-enhanced clustering.
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