Ryan Sweke

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Jens EisertProfessor of Quantum Physics at Freie Universität Berlin, Fraunhofer HHI and Helmholtz Center BerlinVerified email at physik.fu-berlin.de
Johannes Jakob MeyerGraduate Student, Dahlem Center for Complex Quantum Systems, FU BerlinVerified email at fu-berlin.de
Francesco PetruccioneStellenbosch University and National Institute for Theoretical and Computational Sciences (NITheCS)Verified email at sun.ac.za
Ilya SinayskiyQuantum Research Group, School of Chemistry and Physics, UKZN, (WST)Verified email at ukzn.ac.za
Maria SchuldQuantum Machine Learning research lead at XanaduVerified email at xanadu.ai
Dominik HangleiterQuICS, University of MarylandVerified email at umd.edu
Jean-Pierre SeifertProfessor for Computer & Communication Security, TU Berlin & T-LabsVerified email at tu-berlin.de
Frederik WildeFreie Universität BerlinVerified email at fu-berlin.de
Paul K. FaehrmannGraduate Student at Freie Universität BerlinVerified email at fu-berlin.de
Barthelemy Meynard-PiganeauSorbonne UniversityVerified email at polytechnique.edu
Marcel HinschePhD student, Freie Universität BerlinVerified email at fu-berlin.de
Alexander NietnerFreie Universität BerlinVerified email at fu-berlin.de
Nicola PancottiQuantum Research Scientist at AWS & California Institute of TechnologyVerified email at amazon.com
ignacio ciracMax Planck Institute of Quantum OpticsVerified email at mpq.mpg.de
Jonas HaferkampHarvard UniversityVerified email at seas.harvard.edu
Matthias C. CaroPostdoctoral researcher, California Institute of Technology and Freie Universität BerlinVerified email at caltech.edu
Ingo RothQuantum research center, Technology Innovation Institute, Abu Dhabi, UAEVerified email at fu-berlin.de
Elies Gil-Fustergraduate student, Freie University BerlinVerified email at zedat.fu-berlin.de
Denis BERNARDDirecteur de recherche - CNRS & LPENSVerified email at ens.fr
Yihui QuekHarvard UniversityVerified email at fas.harvard.edu

Ryan Sweke

Research Scientist at IBM

Verified email at ibm.com

Quantum Information Quantum Computing


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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	335	2021
Stochastic gradient descent for hybrid quantum-classical optimization R Sweke, F Wilde, J Meyer, M Schuld, PK Fährmann, ... Quantum 4, 314, 2020	197	2020
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	105	2019
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	90	2020
On the quantum versus classical learnability of discrete distributions R Sweke, JP Seifert, D Hangleiter, J Eisert Quantum 5, 417, 2021	68	2021
Encoding-dependent generalization bounds for parametrized quantum circuits MC Caro, E Gil-Fuster, JJ Meyer, J Eisert, R Sweke Quantum 5, 582, 2021	63	2021
Universal simulation of Markovian open quantum systems R Sweke, I Sinayskiy, D Bernard, F Petruccione Physical Review A 91 (6), 062308, 2015	52	2015
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	50	2016
Dissipative preparation of large W states in optical cavities R Sweke, I Sinayskiy, F Petruccione Physical Review A 87 (4), 042323, 2013	42	2013
Simulation of single-qubit open quantum systems R Sweke, I Sinayskiy, F Petruccione Physical Review A 90 (2), 022331, 2014	39	2014
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	23	2019
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	12*	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	12	2021
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	12	2013
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	11	2022
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	7	2022
Superpolynomial quantum-classical separation for density modeling N Pirnay, R Sweke, J Eisert, JP Seifert Physical Review A 107 (4), 042416, 2023	6	2023
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	3	2023
Classical Verification of Quantum Learning MC Caro, M Hinsche, M Ioannou, A Nietner, R Sweke arXiv preprint arXiv:2306.04843, 2023	1	2023

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