| Identifying quantum phase transitions with adversarial neural networks P Huembeli, A Dauphin, P Wittek Physical Review B 97 (13), 134109, 2018 | 137 | 2018 |
| Characterizing the loss landscape of variational quantum circuits P Huembeli, A Dauphin Quantum Science and Technology 6 (2), 025011, 2021 | 94 | 2021 |
| Unsupervised phase discovery with deep anomaly detection K Kottmann, P Huembeli, M Lewenstein, A Acín Physical Review Letters 125 (17), 170603, 2020 | 92 | 2020 |
| Modern applications of machine learning in quantum sciences A Dawid, J Arnold, B Requena, A Gresch, M Płodzień, K Donatella, ... arXiv preprint arXiv:2204.04198, 2022 | 79 | 2022 |
| Automated discovery of characteristic features of phase transitions in many-body localization P Huembeli, A Dauphin, P Wittek, C Gogolin Physical review B 99 (10), 104106, 2019 | 49 | 2019 |
| QuCumber: wavefunction reconstruction with neural networks MJS Beach, IJS De Vlugt, A Golubeva, P Huembeli, B Kulchytskyy, X Luo, ... SciPost Physics 7 (1), 009, 2019 | 42 | 2019 |
| Phase detection with neural networks: interpreting the black box A Dawid, P Huembeli, M Tomza, M Lewenstein, A Dauphin New Journal of Physics 22 (11), 115001, 2020 | 35 | 2020 |
| Avoiding local minima in variational quantum algorithms with neural networks J Rivera-Dean, P Huembeli, A Acín, J Bowles arXiv preprint arXiv:2104.02955, 2021 | 32 | 2021 |
| The physics of energy-based models P Huembeli, JM Arrazola, N Killoran, M Mohseni, P Wittek Quantum Machine Intelligence 4 (1), 1, 2022 | 19 | 2022 |
| Entanglement forging with generative neural network models P Huembeli, G Carleo, A Mezzacapo arXiv preprint arXiv:2205.00933, 2022 | 19 | 2022 |
| Quadratic unconstrained binary optimization via quantum-inspired annealing J Bowles, A Dauphin, P Huembeli, J Martinez, A Acín Physical Review Applied 18 (3), 034016, 2022 | 16 | 2022 |
| Hessian-based toolbox for reliable and interpretable machine learning in physics A Dawid, P Huembeli, M Tomza, M Lewenstein, A Dauphin Machine Learning: Science and Technology 3 (1), 015002, 2021 | 15 | 2021 |
| Modern applications of machine learning in quantum sciences. 2022. doi: 10.48550 A Dawid, J Arnold, B Requena, A Gresch, M Płodzień, K Donatella, ... arXiv preprint ARXIV.2204.04198, 0 | 11 | |
| Exploring quantum perceptron and quantum neural network structures with a teacher-student scheme A Gratsea, P Huembeli Quantum Machine Intelligence 4 (1), 2, 2022 | 10 | 2022 |
| Modern applications of machine learning in quantum sciences (2022) A Dawid, J Arnold, B Requena, A Gresch, M Płodzien, K Donatella, ... arXiv preprint arXiv:2204.04198, 0 | 10 | |
| Towards a heralded eigenstate-preserving measurement of multi-qubit parity in circuit QED P Huembeli, SE Nigg Physical Review A 96 (1), 012313, 2017 | 7 | 2017 |
| Towards a scalable discrete quantum generative adversarial neural network S Chaudhary, P Huembeli, I MacCormack, TL Patti, J Kossaifi, A Galda Quantum Science and Technology 8 (3), 035002, 2023 | 6 | 2023 |
| Modern applications of machine learning in quantum sciences, arXiv e-prints A Dawid, J Arnold, B Requena, A Gresch, M Płodzień, K Donatella, ... arXiv preprint arXiv:2204.04198, 2022 | 6 | 2022 |
| ``Entanglement forging with generative neural network models''(2022) P Huembeli, G Carleo, A Mezzacapo arXiv preprint arXiv:2205.00933, 0 | 5 | |
| The effect of the processing and measurement operators on the expressive power of quantum models A Gratsea, P Huembeli Quantum Machine Intelligence 5 (2), 32, 2023 | 3 | 2023 |
Alexandre DauphinPasqalpasqal.com üzerinde doğrulanmış e-posta adresine sahip