Three learning stages and accuracy–efficiency tradeoff of restricted Boltzmann machines
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DOI: 10.1038/s41467-022-33126-x
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References listed on IDEAS
- Rongxin Xia & Sabre Kais, 2018. "Quantum machine learning for electronic structure calculations," Nature Communications, Nature, vol. 9(1), pages 1-6, December.
- Xun Gao & Lu-Ming Duan, 2017. "Efficient representation of quantum many-body states with deep neural networks," Nature Communications, Nature, vol. 8(1), pages 1-6, December.
- Kenny Choo & Antonio Mezzacapo & Giuseppe Carleo, 2020. "Fermionic neural-network states for ab-initio electronic structure," Nature Communications, Nature, vol. 11(1), pages 1-7, December.
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- Nihal Sanjay Singh & Keito Kobayashi & Qixuan Cao & Kemal Selcuk & Tianrui Hu & Shaila Niazi & Navid Anjum Aadit & Shun Kanai & Hideo Ohno & Shunsuke Fukami & Kerem Y. Camsari, 2024. "CMOS plus stochastic nanomagnets enabling heterogeneous computers for probabilistic inference and learning," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
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