70 citations to https://www.mathnet.ru/eng/prl4
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Felipe F. Fanchini, Göktuğ Karpat, Daniel Z. Rossatto, Ariel Norambuena, Raúl Coto, “Estimating the degree of non-Markovianity using machine learning”, Phys. Rev. A, 103:2 (2021)
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Hongfei Wang, Xiujuan Zhang, Jinguo Hua, Dangyuan Lei, Minghui Lu, Yanfeng Chen, “Topological physics of non-Hermitian optics and photonics: a review”, J. Opt., 23:12 (2021), 123001
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Sreetama Das, Sudipto Singha Roy, Samyadeb Bhattacharya, Ujjwal Sen, “Nearly Markovian maps and entanglement-based bound on corresponding non-Markovianity”, J. Phys. A: Math. Theor., 54:39 (2021), 395301
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Luis E. Herrera Rodríguez, Alexei A. Kananenka, “Convolutional Neural Networks for Long Time Dissipative Quantum Dynamics”, J. Phys. Chem. Lett., 12:9 (2021), 2476
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Scott Davidson, Felix A. Pollock, Erik Gauger, “Principles underlying efficient exciton transport unveiled by information-geometric analysis”, Phys. Rev. Research, 3:3 (2021)
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Philip Taranto, Felix A. Pollock, Kavan Modi, “Non-Markovian memory strength bounds quantum process recoverability”, npj Quantum Inf, 7:1 (2021)
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Dominikus Brian, Xiang Sun, “Generalized quantum master equation: A tutorial review and recent advances”, Chinese Journal of Chemical Physics, 34:5 (2021), 497
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Rodrigo A Vargas-Hernández, Ricky T Q Chen, Kenneth A Jung, Paul Brumer, “Fully differentiable optimization protocols for non-equilibrium steady states”, New J. Phys., 23:12 (2021), 123006
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Andrea Smirne, Nina Megier, Bassano Vacchini, “On the connection between microscopic description and memory effects in open quantum system dynamics”, Quantum, 5 (2021), 439
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Simon Milz, Kavan Modi, “Quantum Stochastic Processes and Quantum non-Markovian Phenomena”, PRX Quantum, 2:3 (2021)