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)
Markus Schmitt, Zala Lenarčič, “From observations to complexity of quantum states via unsupervised learning”, Phys. Rev. B, 106:4 (2022)
Shichen Cao, Jingjing Li, Kenric P. Nelson, Mark A. Kon, “Coupled VAE: Improved Accuracy and Robustness of a Variational Autoencoder”, Entropy, 24:3 (2022), 423
Atithi Acharya, Siddhartha Saha, Anirvan M. Sengupta, “Shadow tomography based on informationally complete positive operator-valued measure”, Phys. Rev. A, 104:5 (2021)
Juan Carrasquilla, Di Luo, Felipe Pérez, Ashley Milsted, Bryan K. Clark, Maksims Volkovs, Leandro Aolita, “Probabilistic simulation of quantum circuits using a deep-learning architecture”, Phys. Rev. A, 104:3 (2021)