Corpus ID: 220302651

Boltzmann machine learning with a variational quantum algorithm

@article{Shingu2020BoltzmannML,
  title={Boltzmann machine learning with a variational quantum algorithm},
  author={Yuta Shingu and Yuya Seki and S. Watabe and Suguru Endo and Y. Matsuzaki and S. Kawabata and T. Nikuni and H. Hakoshima},
  journal={arXiv: Quantum Physics},
  year={2020}
}
Boltzmann machine is a powerful tool for modeling probability distributions that govern the training data. A thermal equilibrium state is typically used for Boltzmann machine learning to obtain a suitable probability distribution. The Boltzmann machine learning consists of calculating the gradient of the loss function given in terms of the thermal average, which is the most time consuming procedure. Here, we propose a method to implement the Boltzmann machine learning by using Noisy… Expand
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