62 citations to https://www.mathnet.ru/eng/prl3
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J Barr, G Zicari, A Ferraro, M Paternostro, “Spectral density classification for environment spectroscopy”, Mach. Learn.: Sci. Technol., 5:1 (2024), 015043
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Anton Trushechkin, “Long-term behaviour in an exactly solvable model of pure decoherence and the problem of Markovian embedding”, Mathematics, 12:1 (2024), 1–18
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Lucas B. Vieira, Simon Milz, Giuseppe Vitagliano, Costantino Budroni, “Witnessing environment dimension through temporal correlations”, Quantum, 8 (2024), 1224
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Sergei Kuzmin, Varvara Mikhailova, Ivan Dyakonov, Stanislav Straupe, “Learning the tensor network model of a quantum state using a few single-qubit measurements”, Phys. Rev. A, 109:5 (2024)
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Philip Taranto, Marco Túlio Quintino, Mio Murao, Simon Milz, “Characterising the Hierarchy of Multi-time Quantum Processes with Classical Memory”, Quantum, 8 (2024), 1328
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P Figueroa-Romero, M Papič, A Auer, M-H Hsieh, K Modi, I de Vega, “Operational Markovianization in randomized benchmarking”, Quantum Sci. Technol., 9:3 (2024), 035020
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He-Ran Wang, Xiao-Yang Yang, Zhong Wang, “Exact Hidden Markovian Dynamics in Quantum Circuits”, Phys. Rev. Lett., 133:17 (2024)
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Youssef Kora, Hadi Zadeh-Haghighi, Terrence C. Stewart, Khabat Heshami, Christoph Simon, “Frequency- and dissipation-dependent entanglement advantage in spin-network quantum reservoir computing”, Phys. Rev. A, 110:4 (2024)
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A. E. Teretenkov, “Quantum Markovian dynamics after the bath correlation time”, Comput. Math. Math. Phys., 63:1 (2023), 135–145
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Yu Yao, Chao Cao, Stephan Haas, Mahak Agarwal, Divyam Khanna, Marcin Abram, “Emulating quantum dynamics with neural networks via knowledge distillation”, Front. Mater., 9 (2023)