Leonard Wossnig
TitleCited byYear
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
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
https://arxiv.org/abs/1612.01789, 2017
Universal discriminative quantum neural networks
H Chen, L Wossnig, S Severini, H Neven, M Mohseni
arXiv preprint arXiv:1805.08654, 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
Quantum linear systems algorithms: a primer
D Dervovic, M Herbster, P Mountney, S Severini, N Usher, L Wossnig
arXiv preprint arXiv:1802.08227, 2018
An initialization strategy for addressing barren plateaus in parametrized quantum circuits
E Grant, L Wossnig, M Ostaszewski, M Benedetti
arXiv preprint arXiv:1903.05076, 2019
A quantum algorithm for simulating non-sparse Hamiltonians
C Wang, L Wossnig
arXiv preprint arXiv:1803.08273, 2018
The Role of Information in Group Formation.
S Bennati, L Wossnig, J Thiele
ICAART (1), 231-235, 2016
Generative training of quantum Boltzmann machines with hidden units
N Wiebe, L Wossnig
arXiv preprint arXiv:1905.09902, 2019
Quantum-classical truncated Newton method for high-dimensional energy landscapes
L Wossnig, S Tschiatschek, S Zohren
arXiv preprint arXiv:1710.07063, 2017
Quantum machine learning: Challenges and Opportunities
L Wossnig, S Severini
APS Meeting Abstracts, 2019
Approximating Hamiltonian dynamics with the Nystr\" om method
A Rudi, L Wossnig, C Ciliberto, A Rocchetto, M Pontil, S Severini
arXiv preprint arXiv:1804.02484, 2018
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Articles 1–13