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Ryan Sweke
Ryan Sweke
Research Scientist at IBM
Bestätigte E-Mail-Adresse bei ibm.com
Titel
Zitiert von
Zitiert von
Jahr
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
3822021
Stochastic gradient descent for hybrid quantum-classical optimization
R Sweke, F Wilde, J Meyer, M Schuld, PK Fährmann, ...
Quantum 4, 314, 2020
2112020
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
1082019
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
1002020
On the quantum versus classical learnability of discrete distributions
R Sweke, JP Seifert, D Hangleiter, J Eisert
Quantum 5, 417, 2021
782021
Encoding-dependent generalization bounds for parametrized quantum circuits
MC Caro, E Gil-Fuster, JJ Meyer, J Eisert, R Sweke
Quantum 5, 582, 2021
772021
Universal simulation of Markovian open quantum systems
R Sweke, I Sinayskiy, D Bernard, F Petruccione
Physical Review A 91 (6), 062308, 2015
542015
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
522016
Dissipative preparation of large W states in optical cavities
R Sweke, I Sinayskiy, F Petruccione
Physical Review A 87 (4), 042323, 2013
422013
Simulation of single-qubit open quantum systems
R Sweke, I Sinayskiy, F Petruccione
Physical Review A 90 (2), 022331, 2014
392014
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
24*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
232019
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
16*2023
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
122021
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
122013
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
112022
Superpolynomial quantum-classical separation for density modeling
N Pirnay, R Sweke, J Eisert, JP Seifert
Physical Review A 107 (4), 042416, 2023
82023
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
72023
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
72022
Classical Verification of Quantum Learning
MC Caro, M Hinsche, M Ioannou, A Nietner, R Sweke
arXiv preprint arXiv:2306.04843, 2023
42023
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