Articoli con mandati relativi all'accesso pubblico - Leonard WossnigUlteriori informazioni
Disponibili pubblicamente: 12
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
Mandati: National Natural Science Foundation of China, UK Engineering and Physical …
Quantum machine learning: a classical perspective
C Ciliberto, M Herbster, AD Ialongo, M Pontil, A Rocchetto, S Severini, ...
Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2018
Mandati: National Natural Science Foundation of China, UK Engineering and Physical …
Quantum linear system algorithm for dense matrices
L Wossnig, Z Zhao, A Prakash
Physical review letters 120 (5), 050502, 2018
Mandati: National Research Foundation, Singapore
Quantum gradient descent and Newton's method for constrained polynomial optimization
P Rebentrost, M Schuld, L Wossnig, F Petruccione, S Lloyd
https://arxiv.org/abs/1612.01789, 2017
Mandati: US Department of Defense, National Research Foundation, South Africa
Adversarial quantum circuit learning for pure state approximation
M Benedetti, E Grant, L Wossnig, S Severini
New Journal of Physics 21 (4), 043023, 2019
Mandati: US Department of Defense, National Natural Science Foundation of China, UK …
Universal discriminative quantum neural networks
H Chen, L Wossnig, S Severini, H Neven, M Mohseni
Quantum Machine Intelligence 3, 1-11, 2021
Mandati: US Department of Defense, National Natural Science Foundation of China, UK …
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
Mandati: UK Engineering and Physical Sciences Research Council
Quantum state discrimination using noisy quantum neural networks
A Patterson, H Chen, L Wossnig, S Severini, D Browne, I Rungger
Physical Review Research 3 (1), 013063, 2021
Mandati: UK Engineering and Physical Sciences Research Council
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
Mandati: UK Engineering and Physical Sciences Research Council
Approximating Hamiltonian dynamics with the Nyström method
A Rudi, L Wossnig, C Ciliberto, A Rocchetto, M Pontil, S Severini
Quantum 4, 234, 2020
Mandati: US Department of Defense, National Natural Science Foundation of China, UK …
Statistical limits of supervised quantum learning
C Ciliberto, A Rocchetto, A Rudi, L Wossnig
Physical Review A 102 (4), 042414, 2020
Mandati: US National Science Foundation
Best practices for machine learning in antibody discovery and development
L Wossnig, N Furtmann, A Buchanan, S Kumar, V Greiff
Drug Discovery Today, 104025, 2024
Mandati: European Commission, Research Council of Norway
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