| 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 | 37 | 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 |
Asel SagingalievaTerra Quantum AG在 terraquantum.swiss 的电子邮件经过验证