| 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 | 60* | 2023 |
| Broadband continuous-variable entanglement generation using a Kerr-free Josephson metamaterial MR Perelshtein, KV Petrovnin, V Vesterinen, SH Raja, I Lilja, M Will, ... Physical Review Applied 18 (2), 024063, 2022 | 55 | 2022 |
| Practical application-specific advantage through hybrid quantum computing M Perelshtein, A Sagingalieva, K Pinto, V Shete, A Pakhomchik, ... arXiv preprint arXiv:2205.04858, 2022 | 36 | 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 | 34 | 2023 |
| Solving Large‐Scale Linear Systems of Equations by a Quantum Hybrid Algorithm MR Perelshtein, AI Pakhomchik, AA Melnikov, AA Novikov, A Glatz, ... Annalen der Physik, 2200082, 2022 | 31* | 2022 |
| Microwave photon detection at parametric criticality K Petrovnin, J Wang, M Perelshtein, P Hakonen, GS Paraoanu PRX Quantum 5 (2), 020342, 2024 | 20 | 2024 |
| Broadband lumped-element Josephson parametric amplifier with single-step lithography T Elo, TS Abhilash, MR Perelshtein, I Lilja, EV Korostylev, PJ Hakonen Applied Physics Letters 114 (15), 2019 | 20 | 2019 |
| 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 | 15 | 2023 |
| Generation and structuring of multipartite entanglement in a Josephson parametric system KV Petrovnin, MR Perelshtein, T Korkalainen, V Vesterinen, I Lilja, ... Advanced Quantum Technologies 6 (1), 2200031, 2023 | 12 | 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 | 11 | 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 | 9 | 2023 |
| Linear ascending metrological algorithm MR Perelshtein, NS Kirsanov, VV Zemlyanov, AV Lebedev, G Blatter, ... Physical Review Research 3 (1), 013257, 2021 | 9 | 2021 |
| 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 | 8 | 2023 |
| Phase estimation algorithm for the multibeam optical metrology VV Zemlyanov, NS Kirsanov, MR Perelshtein, DI Lykov, OV Misochko, ... Scientific Reports 10 (1), 8715, 2020 | 6 | 2020 |
| TQCompressor: improving tensor decomposition methods in neural networks via permutations V Abronin, A Naumov, D Mazur, D Bystrov, K Tsarova, A Melnikov, ... 2024 IEEE 7th International Conference on Multimedia Information Processing …, 2024 | 5 | 2024 |
| Optimized emulation of quantum magnetometry via superconducting qubits NN Gusarov, MR Perelshtein, PJ Hakonen, GS Paraoanu Physical Review A 107 (5), 052609, 2023 | 5 | 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 | 5 | 2023 |
| Solving workflow scheduling problems with QUBO modeling AI Pakhomchik, S Yudin, MR Perelshtein, A Alekseyenko, S Yarkoni arXiv preprint arXiv:2205.04844, 2022 | 5 | 2022 |
| 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 | 4 | 2023 |
| Tensor quantum programming A Termanova, A Melnikov, E Mamenchikov, N Belokonev, S Dolgov, ... New Journal of Physics 26 (12), 123019, 2024 | 3 | 2024 |
Artem A. MelnikovTerra Quantum AGVerified email at terraquantum.swiss
