Hyperparameter optimization of hybrid quantum neural networks for car classification A Sagingalieva, A Kurkin, A Melnikov, D Kuhmistrov, M Perelshtein, ... arXiv preprint arXiv:2205.04878, 2022 | 21 | 2022 |
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 | 20 | 2022 |
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 | 20* | 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 | 19 | 2022 |
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 | 14 | 2019 |
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 | 6 | 2023 |
Linear ascending metrological algorithm MR Perelshtein, NS Kirsanov, VV Zemlyanov, AV Lebedev, G Blatter, ... Physical Review Research 3 (1), 013257, 2021 | 5 | 2021 |
Solving workflow scheduling problems with QUBO modeling AI Pakhomchik, S Yudin, MR Perelshtein, A Alekseyenko, S Yarkoni arXiv preprint arXiv:2205.04844, 2022 | 4 | 2022 |
Phase estimation algorithm for the multibeam optical metrology VV Zemlyanov, NS Kirsanov, MR Perelshtein, DI Lykov, OV Misochko, ... Scientific Reports 10 (1), 8715, 2020 | 4 | 2020 |
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 | 3 | 2023 |
Quantum state preparation using tensor networks AA Melnikov, AA Termanova, SV Dolgov, F Neukart, M Perelshtein Quantum Science and Technology, 2023 | 2 | 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 | 2 | 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 | 2 | 2023 |
Vacuum-induced correlations in superconducting microwave cavity under multiple pump tones T Korkalainen, I Lilja, MR Perelshtein, KV Petrovnin, GS Paraoanu, ... AIP Conference Proceedings 2362 (1), 2021 | 2 | 2021 |
Optimized emulation of quantum magnetometry via superconducting qubits NN Gusarov, MR Perelshtein, PJ Hakonen, GS Paraoanu Physical Review A 107 (5), 052609, 2023 | 1 | 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 | 1 | 2023 |
Hybrid quantum computation architecture for solving a system of linear binary relations A Pakhomchik, M Perelshtein US Patent App. 17/589,670, 2022 | 1 | 2022 |
Microwave photon detection at parametric criticality K Petrovnin, J Wang, M Perelshtein, P Hakonen, GS Paraoanu arXiv preprint arXiv:2308.07084, 2023 | | 2023 |
NISQ-compatible approximate quantum algorithm for unconstrained and constrained discrete optimization MR Perelshtein, AI Pakhomchik, AA Melnikov, M Podobrii, A Termanova, ... arXiv preprint arXiv:2305.14197, 2023 | | 2023 |
Multipartite continuous-variable entanglement generation using Josephson metamaterials M Perelshtein Bulletin of the American Physical Society, 2023 | | 2023 |