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Filippo Vicentini
Filippo Vicentini
Assistant Professor in Quantum Physics and AI, Ecole Polytechnique, Paris
Verified email at polytechnique.edu - Homepage
Title
Cited by
Cited by
Year
Variational Neural-Network Ansatz for Steady States in Open Quantum Systems
F Vicentini, A Biella, N Regnault, C Ciuti
Phys. Rev. Lett 122 (25), 250503, 2019
2352019
NetKet: A machine learning toolkit for many-body quantum systems
D Hofmann, JET Smith, T Westerhout, F Alet, E Davis, S Efthymiou, ...
SoftwareX 10, 100311, 2019
130*2019
Critical slowing down in driven-dissipative Bose-Hubbard lattices
F Vicentini, F Minganti, R Rota, G Orso, C Ciuti
Physical Review A 97 (1), 013853, 2018
1092018
An efficient quantum algorithm for the time evolution of parameterized circuits
S Barison, F Vicentini, G Carleo
Quantum 5, 512, 2021
1022021
Netket 3: Machine learning toolbox for many-body quantum systems
F Vicentini, D Hofmann, A Szabó, D Wu, C Roth, C Giuliani, G Pescia, ...
SciPost Physics Codebases, 007, 2022
902022
Nonlinear Polariton Fluids in a Flatband Reveal Discrete Gap Solitons
V Goblot, B Rauer, F Vicentini, A Le Boité, E Galopin, A Lemaître, ...
Physical Review Letters 123 (11), 113901, 2019
572019
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
482022
mpi4jax: Zero-copy MPI communication of JAX arrays
D Häfner, F Vicentini
Journal of Open Source Software 6 (65), 3419, 2021
282021
From tensor-network quantum states to tensorial recurrent neural networks
D Wu, R Rossi, F Vicentini, G Carleo
Physical Review Research 5 (3), L032001, 2023
262023
Unbiasing time-dependent Variational Monte Carlo by projected quantum evolution
A Sinibaldi, C Giuliani, G Carleo, F Vicentini
Quantum 7, 1131, 2023
222023
Optimal stochastic unraveling of disordered open quantum systems: Application to driven-dissipative photonics lattices
F Vicentini, F Minganti, A Biella, G Orso, C Ciuti
Physical Review A 99 (1), 032115, 2019
222019
Variational Benchmarks for Quantum Many-Body Problems
D Wu, R Rossi, F Vicentini, N Astrakhantsev, F Becca, X Cao, ...
arXiv preprint arXiv:2302.04919, 2023
212023
Variational dynamics as a ground-state problem on a quantum computer
S Barison, F Vicentini, I Cirac, G Carleo
Physical Review Research 4 (4), 043161, 2022
142022
Positive-definite parametrization of mixed quantum states with deep neural networks
F Vicentini, R Rossi, G Carleo
arXiv preprint arXiv:2206.13488, 2022
122022
Machine learning toolbox for quantum many body physics
F Vicentini
Nature Reviews Physics 3 (3), 156-156, 2021
92021
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
9
Learning ground states of gapped quantum Hamiltonians with Kernel Methods
C Giuliani, F Vicentini, R Rossi, G Carleo
Quantum 7, 1096, 2023
72023
Empirical sample complexity of neural network mixed state reconstruction
H Zhao, G Carleo, F Vicentini
Quantum 8, 1358, 2024
42024
Embedding Classical Variational Methods in Quantum Circuits
S Barison, F Vicentini, G Carleo
arXiv preprint arXiv:2309.08666, 2023
32023
Efficiency of neural quantum states in light of the quantum geometric tensor
S Dash, F Vicentini, M Ferrero, A Georges
arXiv preprint arXiv:2402.01565, 2024
12024
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