73 citations to https://www.mathnet.ru/rus/prl4
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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)
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Paolo P. Mazza, Dominik Zietlow, Federico Carollo, Sabine Andergassen, Georg Martius, Igor Lesanovsky, “Machine learning time-local generators of open quantum dynamics”, Phys. Rev. Research, 3:2 (2021)
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Ilya A. Luchnikov, Mikhail E. Krechetov, Sergey N. Filippov, “Riemannian geometry and automatic differentiation for optimization problems of quantum physics and quantum technologies”, New J. Phys., 23 (2021), 73006–25
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Oliver Kaestle, Regina Finsterhoelzl, Andreas Knorr, Alexander Carmele, “Continuous and time-discrete non-Markovian system-reservoir interactions: Dissipative coherent quantum feedback in Liouville space”, Phys. Rev. Research, 3:2 (2021)
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Mikhail Altaisky, Natalia Kaputkina, 2021 5th Scientific School Dynamics of Complex Networks and their Applications (DCNA), 2021, 10
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Chu Guo, Kavan Modi, Dario Poletti, “Tensor-network-based machine learning of non-Markovian quantum processes”, Phys. Rev. A, 102:6 (2020)
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O. V. Morzhin, A. N. Pechen, “Machine learning for finding suboptimal final times and coherent and incoherent controls for an open two-level quantum system”, Lobachevskii J. Math., 41:12 (2020), 2353–2368
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Jiří Svozilík, Raúl Hidalgo-Sacoto, Ievgen I. Arkhipov, “Universal non-Markovianity detection in hybrid open quantum systems”, Sci Rep, 10:1 (2020)