Comparing the Effects of Boltzmann Machines as Associative Memory in Generative Adversarial Networks between Classical and Quantum Samplings-Reference-Cited by-同舟云学术

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Comparing the Effects of Boltzmann Machines as Associative Memory in Generative Adversarial Networks between Classical and Quantum Samplings

Published:2022-07-15 Issue:7 Volume:91 Page:
ISSN:0031-9015
Container-title:Journal of the Physical Society of Japan
language:en
Short-container-title:J. Phys. Soc. Jpn.

Author:

Urushibata Mitsuru¹²,Ohzeki Masayuki¹³⁴,Tanaka Kazuyuki¹

Affiliation:

1. Graduate School of Information Sciences, Tohoku University, Sendai 980-8579, Japan

2. Crosstab Inc., Kawasaki, Kanagawa 211-0068, Japan

3. Sigma-i Co., Ltd., Minato, Tokyo 108-0075, Japan

4. Institute of Innovative Research, Tokyo Institute of Technology, Yokohama 226-8503, Japan

Publisher

Physical Society of Japan

Subject

General Physics and Astronomy

Link

https://journals.jps.jp/doi/pdf/10.7566/JPSJ.91.074008

Reference28 articles.

1. A. Krizhevsky, I. Sutskever, and G. E. Hinton, Advances in Neural Information Processing Systems 25 (NIPS 2012), 2012, p. 1097.

2. Training Products of Experts by Minimizing Contrastive Divergence

3. Near-term quantum-classical associative adversarial networks

4. Quantum-assisted associative adversarial network: applying quantum annealing in deep learning

5. Quantum annealing in the transverse Ising model

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2. Exploration of new chemical materials using black-box optimization with the D-wave quantum annealer;Frontiers in Computer Science;2023-12-12

3. Individual subject evaluated difficulty of adjustable mazes generated using quantum annealing;Frontiers in Computer Science;2023-12-07

4. Efficient Algorithm for Binary Quadratic Problem by Column Generation and Quantum Annealing;Journal of the Physical Society of Japan;2023-11-15

5. Nonnegative/Binary matrix factorization for image classification using quantum annealing;Scientific Reports;2023-10-02

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