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Marcello Benedetti
Marcello Benedetti
Quantinuum
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Año
Parameterized quantum circuits as machine learning models
M Benedetti, E Lloyd, S Sack, M Fiorentini
Quantum Science and Technology 4 (4), 043001, 2019
10862019
An initialization strategy for addressing barren plateaus in parametrized quantum circuits
E Grant, L Wossnig, M Ostaszewski, M Benedetti
Quantum 3, 214, 2019
5032019
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
359*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
325*2018
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
2802018
Structure optimization for parameterized quantum circuits
M Ostaszewski, E Grant, M Benedetti
Quantum 5, 391, 2021
272*2021
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
2722016
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
2402019
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
212*2017
Hardware-efficient variational quantum algorithms for time evolution
M Benedetti, M Fiorentini, M Lubasch
Physical Review Research 3 (3), 033083, 2021
1782021
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
1422022
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
1112018
Adversarial quantum circuit learning for pure state approximation
M Benedetti, E Grant, L Wossnig, S Severini
New Journal of Physics 21 (4), 043023, 2019
992019
Variational inference with a quantum computer
M Benedetti, B Coyle, M Fiorentini, M Lubasch, M Rosenkranz
Physical Review Applied 16 (4), 044057, 2021
492021
Predicting Gibbs-State Expectation Values with Pure Thermal Shadows
L Coopmans, Y Kikuchi, M Benedetti
PRX Quantum 4 (1), 010305, 2023
412023
Protecting expressive circuits with a quantum error detection code
CN Self, M Benedetti, D Amaro
Nature Physics 20 (2), 219-224, 2024
302024
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
28*2023
On the sample complexity of quantum Boltzmann machine learning
L Coopmans, M Benedetti
Communications Physics 7 (1), 274, 2024
212024
Bayesian learning of parameterised quantum circuits
S Duffield, M Benedetti, M Rosenkranz
Machine Learning: Science and Technology 4 (2), 025007, 2023
142023
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
142021
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
Artículos 1–20