Jens Eisert

According to our database, Jens Eisert authored at least 62 papers between 2003 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

On the expressivity of embedding quantum kernels.

Elies Gil-Fuster, Jens Eisert, Vedran Dunjko

Mach. Learn. Sci. Technol., 2024

Artificially intelligent Maxwell's demon for optimal control of open quantum systems.

Paolo Andrea Erdman, Robert Czupryniak, Bibek Bhandari, Andrew N. Jordan, Frank Noé, Jens Eisert, Giacomo Guarnieri

CoRR, 2024

Localized statistics decoding: A parallel decoding algorithm for quantum low-density parity-check codes.

Timo Hillmann, Lucas Berent, Armanda O. Quintavalle, Jens Eisert, Robert Wille, Joschka Roffe

CoRR, 2024

Online learning of quantum processes.

Asad Raza, Matthias C. Caro, Jens Eisert, Sumeet Khatri

CoRR, 2024

Dataset containing raw threshold and runtime simulation data for a paper evaluation on decoding quantum color codes.

Lucas Berent, Lukas Burgholzer, Peter-Jan H. S. Derks, Jens Eisert, Robert Wille

Dataset, 2024

2023

Dataset containing raw simulation data for a paper on decoding bosonic quantum LDPC codes.

Lucas Berent, Timo Hillmann, Jens Eisert, Robert Wille, Joschka Roffe

Dataset, November, 2023

Correcting non-independent and non-identically distributed errors with surface codes.

Konstantin Tiurev, Peter-Jan H. S. Derks, Joschka Roffe, Jens Eisert, Jan-Michael Reiner

Quantum, September, 2023

Anonymous conference key agreement in linear quantum networks.

Jarn de Jong, Frederik Hahn, Jens Eisert, Nathan Walk, Anna Pappa

Quantum, September, 2023

Semi-device-dependent blind quantum tomography.

Ingo Roth, Jadwiga Wilkens, Dominik Hangleiter, Jens Eisert

Quantum, July, 2023

Dataset containing raw threshold and runtime simulation data for a paper evaluation on decoding quantum color codes.

Lucas Berent, Lukas Burgholzer, Peter-Jan H. S. Derks, Jens Eisert, Robert Wille

Dataset, March, 2023

Analog information decoding of bosonic quantum LDPC codes.

Lucas Berent, Timo Hillmann, Jens Eisert, Robert Wille, Joschka Roffe

CoRR, 2023

Potential and limitations of random Fourier features for dequantizing quantum machine learning.

Ryan Sweke, Erik Recio, Sofiène Jerbi, Elies Gil-Fuster, Bryce Fuller, Jens Eisert, Johannes Jakob Meyer

CoRR, 2023

Verifiable measurement-based quantum random sampling with trapped ions.

Martin Ringbauer, Marcel Hinsche, Thomas Feldker, Paul K. Faehrmann, Juani Bermejo-Vega, Claire Edmunds, Lukas Postler, Roman Stricker, Christian D. Marciniak, Michael Meth, Ivan Pogorelov, Rainer Blatt, Philipp Schindler, Jens Eisert, Thomas Monz, Dominik Hangleiter

CoRR, 2023

Understanding quantum machine learning also requires rethinking generalization.

Elies Gil-Fuster, Jens Eisert, Carlos Bravo-Prieto

CoRR, 2023

On the average-case complexity of learning output distributions of quantum circuits.

Alexander Nietner, Marios Ioannou, Ryan Sweke, Richard Kueng, Jens Eisert, Marcel Hinsche, Jonas Haferkamp

CoRR, 2023

Towards provably efficient quantum algorithms for large-scale machine-learning models.

Junyu Liu, Minzhao Liu, Jin-Peng Liu, Ziyu Ye, Yuri Alexeev, Jens Eisert, Liang Jiang

CoRR, 2023

Good Gottesman-Kitaev-Preskill codes from the NTRU cryptosystem.

Jonathan Conrad, Jens Eisert, Jean-Pierre Seifert

CoRR, 2023

2022

Randomizing multi-product formulas for Hamiltonian simulation.

Paul K. Faehrmann, Mark Steudtner, Richard Kueng, Maria Kieferova, Jens Eisert

Quantum, September, 2022

Tensor network models of AdS/qCFT.

Alexander Jahn, Zoltán Zimborás, Jens Eisert

Quantum, 2022

Gottesman-Kitaev-Preskill codes: A lattice perspective.

Jonathan Conrad, Jens Eisert, Francesco Arzani

Quantum, 2022

A super-polynomial quantum advantage for combinatorial optimization problems.

Niklas Pirnay, Vincent Ulitzsch, Frederik Wilde, Jens Eisert, Jean-Pierre Seifert

CoRR, 2022

A super-polynomial quantum-classical separation for density modelling.

Niklas Pirnay, Ryan Sweke, Jens Eisert, Jean-Pierre Seifert

CoRR, 2022

Noise can be helpful for variational quantum algorithms.

Junyu Liu, Frederik Wilde, Antonio Anna Mele, Liang Jiang, Jens Eisert

CoRR, 2022

Scalably learning quantum many-body Hamiltonians from dynamical data.

Frederik Wilde, Augustine Kshetrimayum, Ingo Roth, Dominik Hangleiter, Ryan Sweke, Jens Eisert

CoRR, 2022

A single T-gate makes distribution learning hard.

Marcel Hinsche, Marios Ioannou, Alexander Nietner, Jonas Haferkamp, Yihui Quek, Dominik Hangleiter, Jean-Pierre Seifert, Jens Eisert, Ryan Sweke

CoRR, 2022

Classical surrogates for quantum learning models.

Franz J. Schreiber, Jens Eisert, Johannes Jakob Meyer

CoRR, 2022

Computational advantage of quantum random sampling.

Dominik Hangleiter, Jens Eisert

CoRR, 2022

Exploiting symmetry in variational quantum machine learning.

Johannes Jakob Meyer, Marian Mularski, Elies Gil-Fuster, Antonio Anna Mele, Francesco Arzani, Alissa Wilms, Jens Eisert

CoRR, 2022

A smallest computable entanglement monotone.

Jens Eisert, Mark M. Wilde

Proceedings of the IEEE International Symposium on Information Theory, 2022

2021

On the Quantum versus Classical Learnability of Discrete Distributions.

Ryan Sweke, Jean-Pierre Seifert, Dominik Hangleiter, Jens Eisert

Quantum, 2021

Non-Pauli topological stabilizer codes from twisted quantum doubles.

Julio Carlos Magdalena de la Fuente, Nicolas Tarantino, Jens Eisert

Quantum, 2021

Encoding-dependent generalization bounds for parametrized quantum circuits.

Matthias C. Caro, Elies Gil-Fuster, Johannes Jakob Meyer, Jens Eisert, Ryan Sweke

Quantum, 2021

Reinforcement learning decoders for fault-tolerant quantum computation.

Ryan Sweke, Markus S. Kesselring, Evert P. L. van Nieuwenburg, Jens Eisert

Mach. Learn. Sci. Technol., 2021

Guaranteed blind deconvolution and demixing via hierarchically sparse reconstruction.

Axel Flinth, Ingo Roth, Benedikt Groß, Jens Eisert, Gerhard Wunder

CoRR, 2021

Learnability of the output distributions of local quantum circuits.

Marcel Hinsche, Marios Ioannou, Alexander Nietner, Jonas Haferkamp, Yihui Quek, Dominik Hangleiter, Jean-Pierre Seifert, Jens Eisert, Ryan Sweke

CoRR, 2021

Single-component gradient rules for variational quantum algorithms.

Thomas Hubregtsen, Frederik Wilde, Shozab Qasim, Jens Eisert

CoRR, 2021

Hierarchical compressed sensing.

Jens Eisert, Axel Flinth, Benedikt Groß, Ingo Roth, Gerhard Wunder

CoRR, 2021

Hierarchical Sparse Recovery from Hierarchically Structured Measurements with Application to Massive Random Access.

Benedikt Groß, Axel Flinth, Ingo Roth, Jens Eisert, Gerhard Wunder

Proceedings of the IEEE Statistical Signal Processing Workshop, 2021

2020

Reliable Recovery of Hierarchically Sparse Signals for Gaussian and Kronecker Product Measurements.

Ingo Roth, Martin Kliesch, Axel Flinth, Gerhard Wunder, Jens Eisert

IEEE Trans. Signal Process., 2020

Stochastic gradient descent for hybrid quantum-classical optimization.

Ryan Sweke, Frederik Wilde, Johannes Jakob Meyer, Maria Schuld, Paul K. Faehrmann, Barthélémy Meynard-Piganeau, Jens Eisert

Quantum, 2020

Efficient variational contraction of two-dimensional tensor networks with a non-trivial unit cell.

Alexander Nietner, Bram Vanhecke, Frank Verstraete, Jens Eisert, Laurens Vanderstraeten

Quantum, 2020

By-passing fluctuation theorems.

Paul Boes, Rodrigo Gallego, Nelly Huei Ying Ng, Jens Eisert, Henrik Wilming

Quantum, 2020

Hierarchical sparse recovery from hierarchically structured measurements.

Benedikt Groß, Axel Flinth, Ingo Roth, Jens Eisert, Gerhard Wunder

CoRR, 2020

Tensor network approaches for learning non-linear dynamical laws.

Alex Goeßmann, M. Götte, Ingo Roth, Ryan Sweke, Gitta Kutyniok, Jens Eisert

CoRR, 2020

2019

Expressive power of tensor-network factorizations for probabilistic modeling, with applications from hidden Markov models to quantum machine learning.

Ivan Glasser, Ryan Sweke, Nicola Pancotti, Jens Eisert, J. Ignacio Cirac

CoRR, 2019

Expressive power of tensor-network factorizations for probabilistic modeling.

Ivan Glasser, Ryan Sweke, Nicola Pancotti, Jens Eisert, J. Ignacio Cirac

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Performance of Hierarchical Sparse Detectors for Massive MTC.

Gerhard Wunder, Ingo Roth, Rick Fritschek, Jens Eisert

CoRR, 2018

Recovering quantum gates from few average gate fidelities.

Ingo Roth, Richard Kueng, Shelby Kimmel, Yi-Kai Liu, David Gross, Jens Eisert, Martin Kliesch

CoRR, 2018

Secure massive IoT using hierarchical fast blind deconvolution.

Gerhard Wunder, Ingo Roth, Rick Fritschek, Benedikt Groß, Jens Eisert

Proceedings of the 2018 IEEE Wireless Communications and Networking Conference Workshops, 2018

Hierarchical restricted isometry property for Kronecker product measurements.

Ingo Roth, Axel Flinth, Richard Kueng, Jens Eisert, Gerhard Wunder

Proceedings of the 56th Annual Allerton Conference on Communication, 2018

2017

Axiomatic Characterization of the Quantum Relative Entropy and Free Energy.

Henrik Wilming, Rodrigo Gallego, Jens Eisert

Entropy, 2017

Guaranteed recovery of quantum processes from few measurements.

Martin Kliesch, Richard Kueng, Jens Eisert, David Gross

CoRR, 2017

HiHTP: A custom-tailored hierarchical sparse detector for massive MTC.

Gerhard Wunder, Ingo Roth, Rick Fritschek, Jens Eisert

Proceedings of the 51st Asilomar Conference on Signals, Systems, and Computers, 2017

2016

Improving Compressed Sensing With the Diamond Norm.

Martin Kliesch, Richard Kueng, Jens Eisert, David Gross

IEEE Trans. Inf. Theory, 2016

Reliable recovery of hierarchically sparse signals and application in machine-type communications.

Ingo Roth, Martin Kliesch, Gerhard Wunder, Jens Eisert

CoRR, 2016

Proceedings of the third "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'16).

Vinayak Abrol, Olivier Absil, Pierre-Antoine Absil, Sandrine Anthoine, Philippe Antoine, Thomas Arildsen, Nancy Bertin, Folkert Bleichrodt, Jérôme Bobin, Anne Bol, Antoine Bonnefoy, Francesco Caltagirone, Valerio Cambareri, Cecile Chenot, Vladimir S. Crnojevic, Marie Danková, Kévin Degraux, Jens Eisert, Mohamed-Jalal Fadili, Marylou Gabrié, Nicolas Gac, Daniele Giacobello, Carlos A. Gomez Gonzalez, Adriana Gonzalez, Pierre-Yves Gousenbourger, Mads Græsbøll Christensen, Rémi Gribonval, Stéphanie Guérit, Shaoguang Huang, Paul Irofti, Laurent Jacques, Ulugbek S. Kamilov, Srdan Kitic, Martin Kliesch, Florent Krzakala, John A. Lee, Wenzhi Liao, Tobias Lindstrøm Jensen, Andre Manoel, Hassan Mansour, Ali Mohammad-Djafari, Amirafshar Moshtaghpour, Fred Maurice Ngolè Mboula, Benoît Pairet, Marko Panic, Gabriel Peyré, Aleksandra Pizurica, Pavel Rajmic, Matthieu Roblin, Ingo Roth, Anil Kumar Sao, Pulkit Sharma, Jean-Luc Starck, Eric W. Tramel, Toon van Waterschoot, Dejan Vukobratovic, Li Wang, Benedikt Wirth, Gerhard Wunder, Hongyan Zhang

CoRR, 2016

2014

Majorana fermions and non-locality.

Earl T. Campbell, Matty J. Hoban, Jens Eisert

Quantum Inf. Comput., 2014

2013

Boson-Sampling in the light of sample complexity.

Christian Gogolin, Martin Kliesch, Leandro Aolita, Jens Eisert

CoRR, 2013

2009

Deciding whether a Quantum Channel is Markovian is NP-hard

Toby S. Cubitt, Jens Eisert, Michael M. Wolf

CoRR, 2009

2008

Quantum margulis expanders.

David Gross, Jens Eisert

Quantum Inf. Comput., 2008

2006

Quantum Computing.

Jens Eisert, Michael M. Wolf

Proceedings of the Handbook of Nature-Inspired and Innovative Computing, 2006

2003

Entanglement transformations of pure Gaussian states.

Geza Giedke, Jens Eisert, J. Ignacio Cirac, Martin B. Plenio

Quantum Inf. Comput., 2003