| The variational quantum eigensolver: a review of methods and best practices J Tilly, H Chen, S Cao, D Picozzi, K Setia, Y Li, E Grant, L Wossnig, ... Physics Reports 986, 1-128, 2022 | 957 | 2022 |
| An initialization strategy for addressing barren plateaus in parametrized quantum circuits E Grant, L Wossnig, M Ostaszewski, M Benedetti Quantum 3, 214, 2019 | 493 | 2019 |
| Hierarchical quantum classifiers E Grant, M Benedetti, S Cao, A Hallam, J Lockhart, V Stojevic, AG Green, ... npj Quantum Information 4 (1), 65, 2018 | 320* | 2018 |
| Structure optimization for parameterized quantum circuits M Ostaszewski, E Grant, M Benedetti Quantum 5, 391, 2021 | 231 | 2021 |
| Adversarial quantum circuit learning for pure state approximation M Benedetti, E Grant, L Wossnig, S Severini New Journal of Physics 21 (4), 043023, 2019 | 97 | 2019 |
| Modelling non-markovian quantum processes with recurrent neural networks L Banchi, E Grant, A Rocchetto, S Severini New Journal of Physics 20 (12), 123030, 2018 | 97 | 2018 |
| Learning hard quantum distributions with variational autoencoders A Rocchetto, E Grant, S Strelchuk, G Carleo, S Severini npj Quantum Information 4 (1), 28, 2018 | 83 | 2018 |
| Learning hard quantum distributions with variational autoencoders A Rocchetto, E Grant, S Strelchuk, G Carleo, S Severini NIPS 2017, Machine Learning for Molecules and Materials, 2017 | 83 | 2017 |
| Dynamical mean field theory algorithm and experiment on quantum computers I Rungger, N Fitzpatrick, H Chen, CH Alderete, H Apel, A Cowtan, ... arXiv preprint arXiv:1910.04735, 2019 | 67 | 2019 |
| Reduced density matrix sampling: Self-consistent embedding and multiscale electronic structure on current generation quantum computers J Tilly, PV Sriluckshmy, A Patel, E Fontana, I Rungger, E Grant, ... Physical Review Research 3 (3), 033230, 2021 | 64 | 2021 |
| Quantum circuit structure learning M Ostaszewski, E Grant, M Benedetti arXiv preprint arXiv:1905.09692 41, 2019 | 50 | 2019 |
| Computation of molecular excited states on IBM quantum computers using a discriminative variational quantum eigensolver J Tilly, G Jones, H Chen, L Wossnig, E Grant Physical Review A 102 (6), 062425, 2020 | 40* | 2020 |
| Cost-function embedding and dataset encoding for machine learning with parametrized quantum circuits S Cao, L Wossnig, B Vlastakis, P Leek, E Grant Physical Review A 101 (5), 052309, 2020 | 33 | 2020 |
| Deep disentangled representations for volumetric reconstruction E Grant, P Kohli, M Van Gerven Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8 …, 2016 | 32 | 2016 |
| Compact neural networks based on the multiscale entanglement renormalization ansatz A Hallam, E Grant, V Stojevic, S Severini, AG Green BMVC 2018, 2017 | 14 | 2017 |
| Machine learning logical gates for quantum error correction H Chen, M Vasmer, NP Breuckmann, E Grant arXiv preprint arXiv:1912.10063, 2019 | 10 | 2019 |
| Predicting and visualizing psychological attributions with a deep neural network E Grant, S Sahm, M Zabihi, M van Gerven 2016 23rd International Conference on Pattern Recognition (ICPR), 1-6, 2016 | 4 | 2016 |
| Quantum computing system and method S Cao, H Chen, E Grant, J Tilly US Patent App. 18/271,918, 2024 | 1 | 2024 |
| Method for Identifying a Valid Energy State JLG Tilly, EN Grant US Patent App. 17/147,879, 2021 | 1 | 2021 |
| Advances in Quantum Machine Learning E Grant UCL (University College London), 2024 | | 2024 |
Marcello BenedettiQuantinuumE-mailová adresa ověřena na: quantinuum.com
