22 citations to https://www.mathnet.ru/eng/njp3
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Xuan Li, Zongsheng Zhou, Guanglei Xu, Runze Chi, Yibin Guo, Tong Liu, Haijun Liao, Tao Xiang, “Accurate determination of low-energy eigenspectra with multitarget matrix product states”, Phys. Rev. B, 109:4 (2024)
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I. A. Luchnikov, M. A. Gavreev, A. K. Fedorov, “Controlling quantum many-body systems using reduced-order modeling”, Phys. Rev. Research, 6:1 (2024)
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Manuel S Rudolph, Jing Chen, Jacob Miller, Atithi Acharya, Alejandro Perdomo-Ortiz, “Decomposition of matrix product states into shallow quantum circuits”, Quantum Sci. Technol., 9:1 (2024), 015012
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Qiang Miao, Thomas Barthel, “Isometric tensor network optimization for extensive Hamiltonians is free of barren plateaus”, Phys. Rev. A, 109:5 (2024)
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Ryo Watanabe, Hiroshi Ueda, “Automatic structural search of tensor network states including entanglement renormalization”, Phys. Rev. Research, 6:3 (2024)
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Jonas B. Rigo, Andrew K. Mitchell, “Unsupervised learning of effective quantum impurity models”, Phys. Rev. Research, 6:4 (2024)
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Dominik Hangleiter, Ingo Roth, Jonáš Fuksa, Jens Eisert, Pedram Roushan, “Robustly learning the Hamiltonian dynamics of a superconducting quantum processor”, Nat Commun, 15:1 (2024)
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Yusuke Ono, Linyu Peng, “The Comparison of Riemannian Geometric Matrix-CFAR Signal Detectors”, IEEE Trans. Aerosp. Electron. Syst., 2023, 1
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Hans-Martin Rieser, Frank Köster, Arne Peter Raulf, “Tensor networks for quantum machine learning”, Proc. R. Soc. A., 479:2275 (2023)
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M. R. Perelshtein, A. I. Pakhomchik, Ar. A. Melnikov, M. Podobrii, A. Termanova, I. Kreidich, B. Nuriev, S. Iudin, C. W. Mansell, V. M. Vinokur, “NISQ-compatible approximate quantum algorithm for unconstrained and constrained discrete optimization”, Quantum, 7 (2023), 1186