Machine learning methods in quantum computing theory

D. V. Fastovets; Yu. I. Bogdanov; B. I. Bantysh; V. F. Lukichev

doi:10.1117/12.2522427

Translator Disclaimer

15 March 2019 Machine learning methods in quantum computing theory

D. V. Fastovets, Yu. I. Bogdanov, B. I. Bantysh, V. F. Lukichev

Author Affiliations +

Proceedings Volume 11022, International Conference on Micro- and Nano-Electronics 2018; 110222S (2019) https://doi.org/10.1117/12.2522427
Event: The International Conference on Micro- and Nano-Electronics 2018, 2018, Zvenigorod, Russian Federation

Abstract

Classical machine learning theory and theory of quantum computations are among of the most rapidly developing scientific areas in our days. In recent years, researchers investigated if quantum computing can help to improve classical machine learning algorithms. The quantum machine learning includes hybrid methods that involve both classical and quantum algorithms. Quantum approaches can be used to analyze quantum states instead of classical data. On other side, quantum algorithms can exponentially improve classical data science algorithm. Here, we show basic ideas of quantum machine learning. We present several new methods that combine classical machine learning algorithms and quantum computing methods. We demonstrate multiclass tree tensor network algorithm, and its approbation on IBM quantum processor. Also, we introduce neural networks approach to quantum tomography problem. Our tomography method allows us to predict quantum state excluding noise influence. Such classical-quantum approach can be applied in various experiments to reveal latent dependence between input data and output measurement results.

Citation Download Citation

D. V. Fastovets, Yu. I. Bogdanov, B. I. Bantysh, and V. F. Lukichev "Machine learning methods in quantum computing theory", Proc. SPIE 11022, International Conference on Micro- and Nano-Electronics 2018, 110222S (15 March 2019); https://doi.org/10.1117/12.2522427

ACCESS THE FULL ARTICLE