Seguir
Ryan Sweke
Ryan Sweke
Research Scientist at IBM
Dirección de correo verificada de ibm.com
Título
Citado por
Citado por
Año
Effect of data encoding on the expressive power of variational quantum-machine-learning models
M Schuld, R Sweke, JJ Meyer
Physical Review A 103 (3), 032430, 2021
5202021
Stochastic gradient descent for hybrid quantum-classical optimization
R Sweke, F Wilde, J Meyer, M Schuld, PK Fährmann, ...
Quantum 4, 314, 2020
2492020
Expressive power of tensor-network factorizations for probabilistic modeling
I Glasser, R Sweke, N Pancotti, J Eisert, I Cirac
Advances in Neural Information Processing Systems, 1496-1508, 2019
1302019
Reinforcement learning decoders for fault-tolerant quantum computation
R Sweke, MS Kesselring, EPL van Nieuwenburg, J Eisert
Machine Learning: Science and Technology 2 (2), 025005, 2020
1112020
Encoding-dependent generalization bounds for parametrized quantum circuits
MC Caro, E Gil-Fuster, JJ Meyer, J Eisert, R Sweke
Quantum 5, 582, 2021
982021
On the quantum versus classical learnability of discrete distributions
R Sweke, JP Seifert, D Hangleiter, J Eisert
Quantum 5, 417, 2021
972021
Universal simulation of Markovian open quantum systems
R Sweke, I Sinayskiy, D Bernard, F Petruccione
Physical Review A 91 (6), 062308, 2015
692015
Digital quantum simulation of many-body non-Markovian dynamics
R Sweke, M Sanz, I Sinayskiy, F Petruccione, E Solano
Physical Review A 94 (2), 022317, 2016
632016
Dissipative preparation of large states in optical cavities
R Sweke, I Sinayskiy, F Petruccione
Physical Review A—Atomic, Molecular, and Optical Physics 87 (4), 042323, 2013
442013
Simulation of single-qubit open quantum systems
R Sweke, I Sinayskiy, F Petruccione
Physical Review A 90 (2), 022331, 2014
422014
Tensor network approaches for learning non-linear dynamical laws
A Goeßmann, M Götte, I Roth, R Sweke, G Kutyniok, J Eisert
First Workshop on Quantum Tensor Networks in Machine Learning, 34th …, 2020
29*2020
Lieb-Robinson bounds for open quantum systems with long-ranged interactions
R Sweke, J Eisert, M Kastner
Journal of Physics A: Mathematical and Theoretical 52 (42), 2019
272019
One Gate Makes Distribution Learning Hard
M Hinsche, M Ioannou, A Nietner, J Haferkamp, Y Quek, D Hangleiter, ...
Physical Review Letters 130 (24), 240602, 2023
252023
Learnability of the output distributions of local quantum circuits
M Hinsche, M Ioannou, A Nietner, J Haferkamp, Y Quek, D Hangleiter, ...
arXiv preprint arXiv:2110.05517, 2021
192021
Scalably learning quantum many-body Hamiltonians from dynamical data
F Wilde, A Kshetrimayum, I Roth, D Hangleiter, R Sweke, J Eisert
arXiv preprint arXiv:2209.14328, 2022
182022
Superpolynomial quantum-classical separation for density modeling
N Pirnay, R Sweke, J Eisert, JP Seifert
Physical Review A 107 (4), 042416, 2023
172023
On the average-case complexity of learning output distributions of quantum circuits
A Nietner, M Ioannou, R Sweke, R Kueng, J Eisert, M Hinsche, ...
arXiv preprint arXiv:2305.05765, 2023
152023
Dissipative preparation of generalized Bell states
R Sweke, I Sinayskiy, F Petruccione
Journal of Physics B: Atomic, Molecular and Optical Physics 46 (10), 104004, 2013
142013
Transparent reporting of research-related greenhouse gas emissions through the scientific CO2nduct initiative
R Sweke, P Boes, N Ng, C Sparaciari, J Eisert, M Goihl
Communications Physics 5 (1), 150, 2022
102022
Potential and limitations of random fourier features for dequantizing quantum machine learning
R Sweke, E Recio, S Jerbi, E Gil-Fuster, B Fuller, J Eisert, JJ Meyer
arXiv preprint arXiv:2309.11647, 2023
82023
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
Artículos 1–20