M. A. Gavreev, A. S. Mastiukova, E. O. Kiktenko, A. K. Fedorov, “Learning entanglement breakdown as a phase transition by confusion”, New J. Phys., 24 (2022), 73045–16
Roy J. Garcia, Kaifeng Bu, Arthur Jaffe, “Quantifying scrambling in quantum neural networks”, J. High Energ. Phys., 2022:3 (2022)
Jie Ren, Zhao Wang, Weixia Chen, Wen-Long You, “Long-range order and quantum criticality in antiferromagnetic chains with long-range staggered interactions”, Phys. Rev. E, 105:3 (2022)
Zakaria Patel, Ejaaz Merali, Sebastian J Wetzel, “Unsupervised learning of Rydberg atom array phase diagram with Siamese neural networks”, New J. Phys., 24:11 (2022), 113021
Jintao Yang, Junpeng Cao, Wen-Li Yang, “Dynamical learning of non-Markovian quantum dynamics”, Chinese Phys. B, 31:1 (2022), 010314
David Yevick, “Variational autoencoder analysis of Ising model statistical distributions and phase transitions”, Eur. Phys. J. B, 95:3 (2022)
David Huber, Oleksandr V Marchukov, Hans-Werner Hammer, Artem G Volosniev, “Morphology of three-body quantum states from machine learning”, New J. Phys., 23:6 (2021), 065009
Oleksandr Balabanov, Mats Granath, “Unsupervised interpretable learning of topological indices invariant under permutations of atomic bands”, Mach. Learn.: Sci. Technol., 2:2 (2021), 025008
Lukas Ruff, Jacob R. Kauffmann, Robert A. Vandermeulen, Gregoire Montavon, Wojciech Samek, Marius Kloft, Thomas G. Dietterich, Klaus-Robert Muller, “A Unifying Review of Deep and Shallow Anomaly Detection”, Proc. IEEE, 109:5 (2021), 756
A Berezutskii, M Beketov, D Yudin, Z Zimborás, J D Biamonte, “Probing criticality in quantum spin chains with neural networks”, J. Phys. Complex., 1:3 (2020), 03LT01