Alexander Nietner

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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
Marcel HinschePhD student, Freie Universität BerlinVerified email at fu-berlin.de
Ryan SwekeGerman Research Chair at African Institute of Mathematical Sciences (AIMS)Verified email at aims.ac.za
Jonas HaferkampHarvard UniversityVerified email at seas.harvard.edu
Dominik HangleiterSimons Institute for the Theory of Computing, UC BerkeleyVerified email at berkeley.edu
Jean-Pierre SeifertEinstein Professor for Computer & Communication Security, TU Berlin & T-LabsVerified email at tu-berlin.de
Yihui QuekMassachusetts Institute of TechnologyVerified email at mit.edu
Laurens VanderstraetenUniversité libre de BruxellesVerified email at ulb.be
Frank VerstraeteTheoretical Physics, DAMTP, University of Cambridge, Ghent UniversityVerified email at cam.ac.uk
Bram VanheckeUniversity of ViennaVerified email at univie.ac.at
Christian KrumnowHTW BerlinVerified email at htw-berlin.de
Emil J. BergholtzStockholm UniversityVerified email at fysik.su.se
Steven J ThomsonUniversity of EdinburghVerified email at ed.ac.uk

Alexander Nietner

Freie Universität Berlin

Verified email at fu-berlin.de - Homepage

Quantum Information Complexity Theory Learning Theory Tensor Networks Quantum Foundations


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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	24	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	20	2021
Efficient variational contraction of two-dimensional tensor networks with a non-trivial unit cell A Nietner, B Vanhecke, F Verstraete, J Eisert, L Vanderstraeten Quantum 4, 328, 2020	20	2020
A single -gate makes distribution learning hard M Hinsche, M Ioannou, A Nietner, J Haferkamp, Y Quek, D Hangleiter, ... arXiv preprint arXiv:2207.03140, 2022	18	2022
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	17	2023
Classical verification of quantum learning MC Caro, M Hinsche, M Ioannou, A Nietner, R Sweke arXiv preprint arXiv:2306.04843, 2023	12	2023
Unifying (quantum) statistical and parametrized (quantum) algorithms A Nietner arXiv preprint arXiv:2310.17716, 2023	11	2023
Tensor types and their usage in physics A Bauer, A Nietner arXiv preprint arXiv:2208.01135, 2020	6	2020
Composite symmetry-protected topological order and effective models A Nietner, C Krumnow, EJ Bergholtz, J Eisert Physical Review B 96 (23), 235138, 2017	6	2017
Local integrals of motion and the stability of many-body localization in Wannier-Stark potentials C Bertoni, J Eisert, A Kshetrimayum, A Nietner, SJ Thomson Physical Review B 109 (2), 024206, 2024	4	2024
Hamiltonian truncation tensor networks for quantum field theories P Schmoll, J Naumann, A Nietner, J Eisert, S Sotiriadis arXiv preprint arXiv:2312.12506, 2023	3	2023
A route towards engineering many-body localization in real materials A Nietner, A Kshetrimayum, J Eisert, B Lake arXiv preprint arXiv:2207.10696, 2022	3	2022
Interactive proofs for verifying (quantum) learning and testing MC Caro, J Eisert, M Hinsche, M Ioannou, A Nietner, R Sweke arXiv preprint arXiv:2410.23969, 2024		2024
Free Fermion Distributions Are Hard to Learn A Nietner arXiv preprint arXiv:2306.04731, 2023		2023
Potential and limitations of probabilistic modelling with quantum circuits R Sweke, J Eisert, Y Quek, M Hinsche, A Nietner, M Ioannou, JP Seifert, ... APS March Meeting Abstracts 2023, S69. 004, 2023		2023
Tensor types and their use in physics A Bauer, A Nietner arXiv preprint arXiv:2208.01135, 2022		2022
Towards a Rigorous Framework to Analyze Quantum Machine Learning A Nietner

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