Bernhard Jobst, Kevin Shen, Carlos A. Riofrío, Elvira Shishenina, Frank Pollmann, “Efficient MPS representations and quantum circuits from the Fourier modes of classical image data”, Quantum, 8 (2024), 1544
A Termanova, Ar Melnikov, E Mamenchikov, N Belokonev, S Dolgov, A Berezutskii, R Ellerbrock, C Mansell, M R Perelshtein, “Tensor quantum programming”, New J. Phys., 26:12 (2024), 123019
Yusuke Ono, Linyu Peng, “The Comparison of Riemannian Geometric Matrix-CFAR Signal Detectors”, IEEE Trans. Aerosp. Electron. Syst., 2023, 1
Hans-Martin Rieser, Frank Köster, Arne Peter Raulf, “Tensor networks for quantum machine learning”, Proc. R. Soc. A., 479:2275 (2023)
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
Shahnawaz Ahmed, Fernando Quijandría, Anton Frisk Kockum, “Gradient-Descent Quantum Process Tomography by Learning Kraus Operators”, Phys. Rev. Lett., 130:15 (2023)
Ar A Melnikov, A A Termanova, S V Dolgov, F Neukart, M R Perelshtein, “Quantum state preparation using tensor networks”, Quantum Sci. Technol., 8:3 (2023), 035027
Evgeniy O. Kiktenko, “Exploring postselection-induced quantum phenomena with time-bidirectional state formalism”, Phys. Rev. A, 107 (2023), 032419
Roeland Wiersema, Nathan Killoran, “Optimizing quantum circuits with Riemannian gradient flow”, Phys. Rev. A, 107:6 (2023)
Qiang Miao, Thomas Barthel, “Quantum-classical eigensolver using multiscale entanglement renormalization”, Phys. Rev. Research, 5:3 (2023)