Applying a Quantum Annealing Based Restricted Boltzmann Machine for MNIST Handwritten Digit Classification

• Autorzy: Kurowski K. , Slysz M. , Subocz M. , Różycki R.

Identyfikatory

DOI

10.12921/cmst.2021.0000011

• Języki publikacji: EN

Abstrakty

EN

in various recent research, there may still be challenges in achieving acceptable performance using quantum computers for solving practical problems. Nevertheless, we demonstrate promising results by using the recent advent of the D-Wave Advantage quantum annealer to train and test a Restricted Boltzmann Machine for the well studied MNIST dataset. We compare our new model with some tests executed on the previous D-Wave 2000Q system and show an improved image classification process with a better overall quality. In this paper we discuss how to enhance often timeconsuming RBM training processes based on the commonly used Gibbs sampling using an improved version of quantum sampling. In order to prevent overfitting we propose some solutions which help to acquire less probable samples from the distribution by adjusting D-wave control and embedding parameters. Finally, we present various limitations of the existing quantum computing hardware and expected changes on the quantum hardware and software sides which can be adopted for further improvements in the field of machine learning.

Słowa kluczowe

EN

machine learning; RBM training; quantum annealing; D-Wave quantum computer; MNIST dataset

• Wydawca: Institute of Bioorganic Chemistry Scientific Publishers OWN, Polish Academy of Sciences
• Czasopismo: Computational Methods in Science and Technology
• Rocznik: 2021
• Tom: Vol. 27, No. 3
• Strony: 99--107
• Opis fizyczny: Bibliogr. 15 poz., rys.

Twórcy

autor

Kurowski K.

• Poznan Supercomputing and Networking Center ul. Jana Pawła II 10 61-139 Poznan, Poland

autor

Slysz M.

• Poznan Supercomputing and Networking Center ul. Jana Pawła II 10 61-139 Poznan, Poland

autor

Subocz M.

• Poznan Supercomputing and Networking Center ul. Jana Pawła II 10 61-139 Poznan, Poland

autor

Różycki R.

• Poznan University of Technology Institute of Computing Science ul. Piotrowo 2, 60-965 Poznan, Poland

Bibliografia

• [1] K. Kurowski, J. Weglarz, M. Subocz, R. Rozycki, G. Waligóra, Hybrid Quantum Annealing Heuristic Method for Solving Job Shop Scheduling Problem, [In:] Computational Science – ICCS 2020. Lecture Notes in Computer Science 12142, Eds. V.V. Krzhizhanovskaya, G. Závodszky, M.H. Lees, J.J. Dongarra, P.M.A. Sloot, S. Brissos, J. Teixeira, Springer, Cham (2020).
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• [15] Scikit-learn documentation, https://scikit-learn.org/stable/modules/generated/sklearn.neuralnetwork.BernoulliRBM.html.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).