Alexey Melnikov

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Asel SagingalievaTerra Quantum AGVerified email at terraquantum.swiss
Mo KordzanganehTerra Quantum AGVerified email at terraquantum.swiss
Hans J BriegelProfessor of Theoretical Physics, University of InnsbruckVerified email at uibk.ac.at
Vedran DunjkoLeiden UniversityVerified email at liacs.leidenuniv.nl
Ray-Kuang LeeNational Tsing Hua UniversityVerified email at ee.nthu.edu.tw
Pavel SekatskiSenior scientist, University of GenevaVerified email at unige.ch
Adi MakmalThe Engineering Faculty, Bar Ilan University, IsraelVerified email at biu.ac.il
Michael R. PerelshteinAalto University, Terra Quantum AGVerified email at aalto.fi
Mario KrennMax Planck Institute for the Science of LightVerified email at mpl.mpg.de
Anton ZeilingerUniversity of ViennaVerified email at univie.ac.at
Hendrik Poulsen NautrupUniversität InnsbruckVerified email at uibk.ac.at
Nicolas SangouardCEAVerified email at cea.fr
Wolfgang DürProfessor of Theoretical Physics, University of InnsbruckVerified email at uibk.ac.at
Andrea SkolikQuantum Computing Scientist at QuiX QuantumVerified email at quixquantum.com
Mario ZimanInstitute of Physics, Slovak Academy of Sciences, BratislavaVerified email at savba.sk
Sergey FilippovAlgorithmiqVerified email at algorithmiq.fi
Gerhard KirchmairUniv. Prof. University of InnsbruckVerified email at uibk.ac.at
Nicolai FriisInstitute of Atomic and Subatomic Physics (Atominstitut), TU WienVerified email at tuwien.ac.at
Florian NeukartProfessorVerified email at umail.leidenuniv.nl
Valerii VinokurTerra Quantum AGVerified email at terraquantum.swiss

Alexey Melnikov

Terra Quantum AG, Switzerland

Verified email at terraquantum.swiss - Homepage

Quantum machine learning Quantum information theory Artificial intelligence


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Active learning machine learns to create new quantum experiments AA Melnikov, HP Nautrup, M Krenn, V Dunjko, M Tiersch, A Zeilinger, ... Proceedings of the National Academy of Sciences 115 (6), 1221-1226, 2018	352	2018
Machine learning for long-distance quantum communication J Wallnöfer, AA Melnikov, W Dür, HJ Briegel PRX quantum 1 (1), 010301, 2020	128	2020
Quantum Machine Learning: from physics to software engineering A Melnikov, M Kordzanganeh, A Alodjants, RK Lee Advances in Physics: X 8 (1), 2165452, 2023	92	2023
Quantum machine learning for image classification A Senokosov, A Sedykh, A Sagingalieva, B Kyriacou, A Melnikov Machine Learning: Science and Technology 5 (1), 015040, 2024	74	2024
Hybrid quantum neural network for drug response prediction A Sagingalieva, M Kordzanganeh, N Kenbayev, D Kosichkina, ... Cancers 15 (10), 2705, 2023	64	2023
Projective simulation with generalization AA Melnikov, A Makmal, V Dunjko, HJ Briegel Scientific reports 7 (1), 14430, 2017	55	2017
Dissociation and annihilation of multipartite entanglement structure in dissipative quantum dynamics SN Filippov, AA Melnikov, M Ziman Physical Review A 88 (6), 062328, 2013	55	2013
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	52*	2023
Predicting quantum advantage by quantum walk with convolutional neural networks AA Melnikov, LE Fedichkin, A Alodjants New Journal of Physics 21 (12), 125002, 2019	52	2019
Setting up experimental Bell tests with reinforcement learning AA Melnikov, P Sekatski, N Sangouard Physical Review Letters 125 (16), 160401, 2020	51	2020
Quantum walks of interacting fermions on a cycle graph AA Melnikov, LE Fedichkin Scientific reports 6 (1), 34226, 2016	50	2016
Coherent controlization using superconducting qubits N Friis, AA Melnikov, G Kirchmair, HJ Briegel Scientific reports 5 (1), 18036, 2015	48	2015
Meta-learning within projective simulation A Makmal, AA Melnikov, V Dunjko, HJ Briegel IEEE Access 4, 2110-2122, 2016	39	2016
Quantum algorithms applied to satellite mission planning for Earth observation S Rainjonneau, I Tokarev, S Iudin, S Rayaprolu, K Pinto, ... IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2023	38	2023
Benchmarking simulated and physical quantum processing units using quantum and hybrid algorithms M Kordzanganeh, M Buchberger, B Kyriacou, M Povolotskii, W Fischer, ... Advanced Quantum Technologies 6 (8), 2300043, 2023	38	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	37	2022
Projective simulation applied to the grid-world and the mountain-car problem AA Melnikov, A Makmal, HJ Briegel arXiv preprint arXiv:1405.5459, 2014	29	2014
Machine learning transfer efficiencies for noisy quantum walks AA Melnikov, LE Fedichkin, RK Lee, A Alodjants Advanced Quantum Technologies 3 (4), 1900115, 2020	25	2020
Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes A Sedykh, M Podapaka, A Sagingalieva, K Pinto, M Pflitsch, A Melnikov Machine Learning: Science and Technology 5 (2), 025045, 2024	24*	2024
Benchmarking projective simulation in navigation problems AA Melnikov, A Makmal, HJ Briegel IEEE Access 6, 64639-64648, 2018	22	2018

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