Michael R. Perelshtein
Cited by
Cited by
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
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
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
Practical application-specific advantage through hybrid quantum computing
M Perelshtein, A Sagingalieva, K Pinto, V Shete, A Pakhomchik, ...
arXiv preprint arXiv:2205.04858, 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
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
Linear ascending metrological algorithm
MR Perelshtein, NS Kirsanov, VV Zemlyanov, AV Lebedev, G Blatter, ...
Physical Review Research 3 (1), 013257, 2021
Solving workflow scheduling problems with QUBO modeling
AI Pakhomchik, S Yudin, MR Perelshtein, A Alekseyenko, S Yarkoni
arXiv preprint arXiv:2205.04844, 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
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
Quantum state preparation using tensor networks
AA Melnikov, AA Termanova, SV Dolgov, F Neukart, M Perelshtein
Quantum Science and Technology, 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
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
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
Optimized emulation of quantum magnetometry via superconducting qubits
NN Gusarov, MR Perelshtein, PJ Hakonen, GS Paraoanu
Physical Review A 107 (5), 052609, 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
Hybrid quantum computation architecture for solving a system of linear binary relations
A Pakhomchik, M Perelshtein
US Patent App. 17/589,670, 2022
Microwave photon detection at parametric criticality
K Petrovnin, J Wang, M Perelshtein, P Hakonen, GS Paraoanu
arXiv preprint arXiv:2308.07084, 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
Multipartite continuous-variable entanglement generation using Josephson metamaterials
M Perelshtein
Bulletin of the American Physical Society, 2023
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