Leonard Wossnig
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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
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
An initialization strategy for addressing barren plateaus in parametrized quantum circuits
E Grant, L Wossnig, M Ostaszewski, M Benedetti
Quantum 3, 214, 2019
Quantum linear system algorithm for dense matrices
L Wossnig, Z Zhao, A Prakash
Physical review letters 120 (5), 050502, 2018
Quantum gradient descent and Newton's method for constrained polynomial optimization
P Rebentrost, M Schuld, L Wossnig, F Petruccione, S Lloyd, 2017
Quantum linear systems algorithms: a primer
D Dervovic, M Herbster, P Mountney, S Severini, N Usher, L Wossnig
arXiv preprint arXiv:1802.08227, 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
Universal discriminative quantum neural networks
H Chen, L Wossnig, S Severini, H Neven, M Mohseni
Quantum Machine Intelligence 3, 1-11, 2021
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
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
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
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
Generative training of quantum Boltzmann machines with hidden units
N Wiebe, L Wossnig
arXiv preprint arXiv:1905.09902, 2019
A quantum algorithm for simulating non-sparse Hamiltonians
C Wang, L Wossnig
arXiv preprint arXiv:1803.08273, 2018
Approximating Hamiltonian dynamics with the Nyström method
A Rudi, L Wossnig, C Ciliberto, A Rocchetto, M Pontil, S Severini
Quantum 4, 234, 2020
Statistical limits of supervised quantum learning
C Ciliberto, A Rocchetto, A Rudi, L Wossnig
Physical Review A 102 (4), 042414, 2020
Quantum machine learning for classical data
L Wossnig
arXiv preprint arXiv:2105.03684, 2021
Quantum relative entropy training of boltzmann machines
NO Wiebe, LP Wossnig
US Patent App. 16/289,417, 2020
Computation of molecular excited states on IBMQ using a Discriminative Variational Quantum Eigensolver
J Tilly, G Jones, H Chen, L Wossnig, E Grant
arXiv e-prints, arXiv: 2001.04941, 2020
Fast quantum learning with statistical guarantees
C Ciliberto, A Rocchetto, A Rudi, L Wossnig
arXiv preprint arXiv:2001.10477, 2020
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