Publication: Quantum approximate optimization and k-means algorithms for data clustering
Issued Date
2021-01-28
Resource Type
ISSN
17426596
17426588
17426588
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2-s2.0-85100772822
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Mahidol University
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SCOPUS
Bibliographic Citation
Journal of Physics: Conference Series. Vol.1719, No.1 (2021)
Suggested Citation
Jirawat Saiphet, Sujin Suwanna, Thiparat Chotibut, Areeya Chantasri Quantum approximate optimization and k-means algorithms for data clustering. Journal of Physics: Conference Series. Vol.1719, No.1 (2021). doi:10.1088/1742-6596/1719/1/012100 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/79019
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Title
Quantum approximate optimization and k-means algorithms for data clustering
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Abstract
Noisy intermediate-scale quantum (NISQ) devices are cutting-edge technology expected to demonstrate potential and advantages of quantum computing over classical computing. Its low number of qubits and imperfection from noises restrict running full-scale quantum algorithms on such devices; however, quantum advantages can still be obtained. To achieve quantum advantages from NISQ devices, the hybrid quantum-classical algorithms were introduced. Quantum approximate optimization algorithm (QAOA) is a variational hybrid algorithm, which utilizes a NISQ device as a sub-unit for specific tasks and performs most calculations on a classical computer. QAOA provides an approximate solution, with arbitrary precision as the number of operations increases, for optimization problems. In this work we investigate the possibility of applying QAOA to a clustering problem and compare its performance with the classical k-means algorithm. It turns out that the weights in graph connectivity can degrade the algorithm operation and make it more difficult to approximate the solution. We also benchmark the QAOA by comparing the approximated solutions with the exact one obtained from a classical clustering algorithm.