| Quantum simulation of parity–time symmetry breaking with a superconducting quantum processor S Dogra, AA Melnikov, GS Paraoanu Communications Physics 4 (1), 26, 2021 | 41 | 2021 |
| Hybrid quantum ResNet for car classification and its hyperparameter optimization A Sagingalieva, M Kordzanganeh, A Kurkin, A Melnikov, D Kuhmistrov, ... Quantum Machine Intelligence 5 (2), 38, 2023 | 34* | 2023 |
| Practical application-specific advantage through hybrid quantum computing M Perelshtein, A Sagingalieva, K Pinto, V Shete, A Pakhomchik, ... arXiv preprint arXiv:2205.04858, 2022 | 28 | 2022 |
| Solving Large‐Scale Linear Systems of Equations by a Quantum Hybrid Algorithm (Ann. Phys. 7/2022). MR Perelshtein, AI Pakhomchik, AA Melnikov, AA Novikov, A Glatz, ... Annalen der Physik 534 (7), 2022 | 22* | 2022 |
| Quantum state preparation using tensor networks AA Melnikov, AA Termanova, SV Dolgov, F Neukart, MR Perelshtein Quantum Science and Technology 8 (3), 035027, 2023 | 16 | 2023 |
| Tetra-aml: automatic machine learning via tensor networks A Naumov, A Melnikov, V Abronin, F Oxanichenko, K Izmailov, M Pflitsch, ... arXiv preprint arXiv:2303.16214, 2023 | 8 | 2023 |
| Protein-protein docking using a tensor train black-box optimization method D Morozov, A Melnikov, V Shete, M Perelshtein arXiv preprint arXiv:2302.03410, 2023 | 5 | 2023 |
| NISQ-compatible approximate quantum algorithm for unconstrained and constrained discrete optimization MR Perelshtein, AI Pakhomchik, AA Melnikov, M Podobrii, A Termanova, ... Quantum 7, 1186, 2023 | 4 | 2023 |
| Numerical solution of the incompressible Navier-Stokes equations for chemical mixers via quantum-inspired Tensor Train Finite Element Method E Kornev, S Dolgov, K Pinto, M Pflitsch, M Perelshtein, A Melnikov arXiv preprint arXiv:2305.10784, 2023 | 4 | 2023 |
| Optimization of chemical mixers design via tensor trains and quantum computing N Belokonev, A Melnikov, M Podapaka, K Pinto, M Pflitsch, M Perelshtein arXiv preprint arXiv:2304.12307, 2023 | 3 | 2023 |
| TQCompressor: improving tensor decomposition methods in neural networks via permutations V Abronin, A Naumov, D Mazur, D Bystrov, K Tsarova, A Melnikov, ... arXiv preprint arXiv:2401.16367, 2024 | 1 | 2024 |
| Comparison between Tensor Networks and Variational Quantum Classifier G Laskaris, AA Melnikov, MR Perelshtein, R Brasher, T Baeck, F Neukart arXiv preprint arXiv:2311.15663, 2023 | 1 | 2023 |
| Tensor Quantum Programming A Termanova, A Melnikov, E Mamenchikov, N Belokonev, S Dolgov, ... arXiv preprint arXiv:2403.13486, 2024 | | 2024 |
Michael R. PerelshteinAalto University, Terra Quantum AGVerified email at aalto.fi
