66 citations to https://www.mathnet.ru/eng/prl4
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Shurui Li, Jianqin Xu, Jing Qian, Weiping Zhang, “Revisiting the dynamics of Bose-Einstein condensates in a double well by deep learning with a hybrid network”, Front. Phys., 17:2 (2022)
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Marco Cattaneo, Gian Luca Giorgi, Roberta Zambrini, Sabrina Maniscalco, “A Brief Journey through Collision Models for Multipartite Open Quantum Dynamics”, Open Syst. Inf. Dyn., 29:03 (2022)
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Xiao Shi, Yun Shang, Chu Guo, “Clustering using matrix product states”, Phys. Rev. A, 105:5 (2022)
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Artem Kryukov, Roman Abramov, Leonid E. Fedichkin, Alexander Alodjants, Alexey A. Melnikov, “Supervised graph classification for chiral quantum walks”, Phys. Rev. A, 105:2 (2022)
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Jintao Yang, Junpeng Cao, Wen-Li Yang, “Dynamical learning of non-Markovian quantum dynamics”, Chinese Phys. B, 31:1 (2022), 010314
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Francesco Ciccarello, Salvatore Lorenzo, Vittorio Giovannetti, G. Massimo Palma, “Quantum collision models: Open system dynamics from repeated interactions”, Physics Reports, 954 (2022), 1
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James Nelson, Luuk Coopmans, Graham Kells, Stefano Sanvito, “Data-driven time propagation of quantum systems with neural networks”, Phys. Rev. B, 106:4 (2022)
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I. A. Luchnikov, E. O. Kiktenko, M. A. Gavreev, H. Ouerdane, S. N. Filippov, A. K. Fedorov, “Probing non-Markovian quantum dynamics with data-driven analysis: Beyond “black-box” machine-learning models”, Phys. Rev. Research, 4 (2022), 43002–22
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Stefano Martina, Stefano Gherardini, Lorenzo Buffoni, Filippo Caruso, “Noise fingerprints in quantum computers: Machine learning software tools”, Software Impacts, 12 (2022), 100260
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Stefano Martina, Lorenzo Buffoni, Stefano Gherardini, Filippo Caruso, “Learning the noise fingerprint of quantum devices”, Quantum Mach. Intell., 4:1 (2022)