Quantum assisted unsupervised data clustering on the basis of neural networks

I. D. Lazarev; Marek Narozniak; Tim Byrnes; A. N. Pyrkov

doi:10.1117/12.2625092

30 January 2022 Quantum assisted unsupervised data clustering on the basis of neural networks

I. D. Lazarev, Marek Narozniak, Tim Byrnes, A. N. Pyrkov

Proceedings Volume 12157, International Conference on Micro- and Nano-Electronics 2021; 1215723 (2022) https://doi.org/10.1117/12.2625092
Event: International Conference on Micro- and Nano-Electronics 2021, 2021, Zvenigorod, Russian Federation

Abstract

Recent progress in machine learning has affected almost all areas of the modern economy. The use of quantum protocols to speed up classical machine learning approaches may have further profound effects on society in the future. Here, we developed a hybrid quantum-assisted self-organizing feature map, a type of artificial neural network, and apply it to the data clustering problem in an unsupervised manner. We show that it allows us to reduce the number of calculations in a number of clusters. It is believed that similar types of hybrid quantum classical algorithms can be the main test bed to achieve practical quantum supremacy on Noisy Intermediate Scale Quantum devices.

Citation Download Citation

I. D. Lazarev, Marek Narozniak, Tim Byrnes, and A. N. Pyrkov "Quantum assisted unsupervised data clustering on the basis of neural networks", Proc. SPIE 12157, International Conference on Micro- and Nano-Electronics 2021, 1215723 (30 January 2022); https://doi.org/10.1117/12.2625092

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KEYWORDS

Machine learning

Neural networks

Quantum information

Quantum computing

Superposition

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