| Parameterized quantum circuits as machine learning models M Benedetti, E Lloyd, S Sack, M Fiorentini Quantum Science and Technology 4 (4), 043001, 2019 | 1000 | 2019 |
| An initialization strategy for addressing barren plateaus in parametrized quantum circuits E Grant, L Wossnig, M Ostaszewski, M Benedetti Quantum 3, 214, 2019 | 467 | 2019 |
| A generative modeling approach for benchmarking and training shallow quantum circuits M Benedetti, D Garcia-Pintos, O Perdomo, V Leyton-Ortega, Y Nam, ... npj Quantum Information 5 (1), 45, 2019 | 346* | 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 | 310* | 2018 |
| Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning M Benedetti, J Realpe-Gómez, R Biswas, A Perdomo-Ortiz Physical Review A 94 (2), 022308, 2016 | 271 | 2016 |
| Structure optimization for parameterized quantum circuits M Ostaszewski, E Grant, M Benedetti Quantum 5, 391, 2021 | 265* | 2021 |
| Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers A Perdomo-Ortiz, M Benedetti, J Realpe-Gómez, R Biswas Quantum Science and Technology 3 (3), 030502, 2018 | 259 | 2018 |
| Training of quantum circuits on a hybrid quantum computer D Zhu, NM Linke, M Benedetti, KA Landsman, NH Nguyen, CH Alderete, ... Science advances 5 (10), eaaw9918, 2019 | 235 | 2019 |
| Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models M Benedetti, J Realpe-Gómez, R Biswas, A Perdomo-Ortiz Physical Review X 7 (4), 041052, 2017 | 207* | 2017 |
| Hardware-efficient variational quantum algorithms for time evolution M Benedetti, M Fiorentini, M Lubasch Physical Review Research 3 (3), 033083, 2021 | 164 | 2021 |
| Filtering variational quantum algorithms for combinatorial optimization D Amaro, C Modica, M Rosenkranz, M Fiorentini, M Benedetti, M Lubasch Quantum Science and Technology 7 (1), 015021, 2022 | 131 | 2022 |
| Quantum-assisted Helmholtz machines: A quantum–classical deep learning framework for industrial datasets in near-term devices M Benedetti, J Realpe-Gómez, A Perdomo-Ortiz Quantum Science and Technology 3 (3), 034007, 2018 | 108 | 2018 |
| Adversarial quantum circuit learning for pure state approximation M Benedetti, E Grant, L Wossnig, S Severini New Journal of Physics 21 (4), 043023, 2019 | 94 | 2019 |
| Variational inference with a quantum computer M Benedetti, B Coyle, M Fiorentini, M Lubasch, M Rosenkranz Physical Review Applied 16 (4), 044057, 2021 | 47 | 2021 |
| Predicting Gibbs-State Expectation Values with Pure Thermal Shadows L Coopmans, Y Kikuchi, M Benedetti PRX Quantum 4 (1), 010305, 2023 | 38 | 2023 |
| Protecting expressive circuits with a quantum error detection code CN Self, M Benedetti, D Amaro Nature Physics 20 (2), 219-224, 2024 | 24 | 2024 |
| Realization of quantum signal processing on a noisy quantum computer Y Kikuchi, C Mc Keever, L Coopmans, M Lubasch, M Benedetti npj Quantum Information 9 (1), 93, 2023 | 22 | 2023 |
| Bayesian learning of parameterised quantum circuits S Duffield, M Benedetti, M Rosenkranz Machine Learning: Science and Technology 4 (2), 025007, 2023 | 14 | 2023 |
| F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuits C Leadbeater, L Sharrock, B Coyle, M Benedetti Entropy 23 (10), 1281, 2021 | 13 | 2021 |
| On the sample complexity of quantum boltzmann machine learning L Coopmans, M Benedetti Communications Physics 7 (1), 274, 2024 | 12 | 2024 |
Alejandro Perdomo-Ortiz--Email verificata su post.harvard.edu