Denis A. Kulikov, Vsevolod I. Yashin, Aleksey K. Fedorov, Evgeniy O. Kiktenko, “Minimizing the negativity of quantum circuits in overcomplete quasiprobability representations”, Phys. Rev. A, 109:1 (2024)
Antonio Ciarlo, David Bronte Ciriza, Martin Selin, Onofrio M. Maragò, Antonio Sasso, Giuseppe Pesce, Giovanni Volpe, Mattias Goksör, “Deep learning for optical tweezers”, Nanophotonics, 13:17 (2024), 3017
Yanming Che, Clemens Gneiting, Franco Nori, “Exponentially improved efficient machine learning for quantum many-body states with provable guarantees”, Phys. Rev. Research, 6:3 (2024)
Jingyang Zheng, Han Kheng Teoh, Michelle L. Delco, Lawrence J. Bonassar, Itai Cohen, Mohamed Yacin Sikkandar, “Application of a variational autoencoder for clustering and analyzing in situ articular cartilage cellular response to mechanical stimuli”, PLoS ONE, 19:5 (2024), e0297947
Hannah Lange, Anka Van de Walle, Atiye Abedinnia, Annabelle Bohrdt, “From architectures to applications: a review of neural quantum states”, Quantum Sci. Technol., 9:4 (2024), 040501
Xiaobing Li, Ranran Guo, Yu Zhou, Kangning Liu, Jia Zhao, Fen Long, Yuanfang Wu, Zhiming Li, “Machine learning phase transitions of the three-dimensional Ising universality class*”, Chinese Phys. C, 47:3 (2023), 034101
Xinzhong Chen, Suheng Xu, Sara Shabani, Yueqi Zhao, Matthew Fu, Andrew J. Millis, Michael M. Fogler, Abhay N. Pasupathy, Mengkun Liu, D. N. Basov, “Machine Learning for Optical Scanning Probe Nanoscopy”, Advanced Materials, 35:34 (2023)
Yiwen Hu, Markus J. Buehler, “Deep language models for interpretative and predictive materials science”, APL Machine Learning, 1:1 (2023)
Guillermo Iglesias, Edgar Talavera, Alberto Díaz-Álvarez, “A survey on GANs for computer vision: Recent research, analysis and taxonomy”, Computer Science Review, 48 (2023), 100553
Yanming Che, Clemens Gneiting, Franco Nori, “Estimating the Euclidean quantum propagator with deep generative modeling of Feynman paths”, Phys. Rev. B, 105:21 (2022)