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2020 – today
- 2024
[c2]Matthias C. Caro
, Marcel Hinsche, Marios Ioannou, Alexander Nietner, Ryan Sweke:
Classical Verification of Quantum Learning. ITCS 2024: 24:1-24:23- 2023
- [i13]Alexander Nietner, Marios Ioannou, Ryan Sweke, Richard Kueng, Jens Eisert, Marcel Hinsche, Jonas Haferkamp:
On the average-case complexity of learning output distributions of quantum circuits. CoRR abs/2305.05765 (2023) - [i12]Matthias C. Caro, Marcel Hinsche, Marios Ioannou, Alexander Nietner, Ryan Sweke:
Classical Verification of Quantum Learning. CoRR abs/2306.04843 (2023) - [i11]Ryan Sweke, Erik Recio, Sofiène Jerbi, Elies Gil-Fuster, Bryce Fuller, Jens Eisert, Johannes Jakob Meyer:
Potential and limitations of random Fourier features for dequantizing quantum machine learning. CoRR abs/2309.11647 (2023) - 2022
- [i10]Marcel Hinsche, Marios Ioannou, Alexander Nietner, Jonas Haferkamp, Yihui Quek, Dominik Hangleiter, Jean-Pierre Seifert, Jens Eisert, Ryan Sweke:
A single T-gate makes distribution learning hard. CoRR abs/2207.03140 (2022) - [i9]Frederik Wilde, Augustine Kshetrimayum, Ingo Roth, Dominik Hangleiter, Ryan Sweke, Jens Eisert:
Scalably learning quantum many-body Hamiltonians from dynamical data. CoRR abs/2209.14328 (2022) - [i8]Niklas Pirnay, Ryan Sweke, Jens Eisert, Jean-Pierre Seifert:
A super-polynomial quantum-classical separation for density modelling. CoRR abs/2210.14936 (2022) - 2021
- [j4]Ryan Sweke, Markus S. Kesselring, Evert P. L. van Nieuwenburg, Jens Eisert:
Reinforcement learning decoders for fault-tolerant quantum computation. Mach. Learn. Sci. Technol. 2(2): 25005 (2021) - [j3]Ryan Sweke, Jean-Pierre Seifert, Dominik Hangleiter, Jens Eisert:
On the Quantum versus Classical Learnability of Discrete Distributions. Quantum 5: 417 (2021) - [j2]Matthias C. Caro, Elies Gil-Fuster, Johannes Jakob Meyer, Jens Eisert, Ryan Sweke:
Encoding-dependent generalization bounds for parametrized quantum circuits. Quantum 5: 582 (2021) - [i7]Matthias C. Caro, Elies Gil-Fuster, Johannes Jakob Meyer, Jens Eisert, Ryan Sweke:
Encoding-dependent generalization bounds for parametrized quantum circuits. CoRR abs/2106.03880 (2021) - [i6]Marcel Hinsche, Marios Ioannou, Alexander Nietner, Jonas Haferkamp, Yihui Quek, Dominik Hangleiter, Jean-Pierre Seifert, Jens Eisert, Ryan Sweke:
Learnability of the output distributions of local quantum circuits. CoRR abs/2110.05517 (2021) - 2020
- [j1]Ryan Sweke, Frederik Wilde, Johannes Jakob Meyer, Maria Schuld, Paul K. Faehrmann, Barthélémy Meynard-Piganeau, Jens Eisert:
Stochastic gradient descent for hybrid quantum-classical optimization. Quantum 4: 314 (2020) - [i5]Alex Goeßmann, M. Götte, Ingo Roth, Ryan Sweke, Gitta Kutyniok, Jens Eisert:
Tensor network approaches for learning non-linear dynamical laws. CoRR abs/2002.12388 (2020) - [i4]Ryan Sweke, Jean-Pierre Seifert, Dominik Hangleiter, Jens Eisert:
On the Quantum versus Classical Learnability of Discrete Distributions. CoRR abs/2007.14451 (2020)
2010 – 2019
- 2019
- [c1]Ivan Glasser, Ryan Sweke, Nicola Pancotti, Jens Eisert, J. Ignacio Cirac:
Expressive power of tensor-network factorizations for probabilistic modeling. NeurIPS 2019: 1496-1508 - [i3]Ivan Glasser, Ryan Sweke, Nicola Pancotti, Jens Eisert, J. Ignacio Cirac:
Expressive power of tensor-network factorizations for probabilistic modeling, with applications from hidden Markov models to quantum machine learning. CoRR abs/1907.03741 (2019) - [i2]Ryan Sweke, Frederik Wilde, Johannes Jakob Meyer, Maria Schuld, Paul K. Faehrmann, Barthélémy Meynard-Piganeau, Jens Eisert:
Stochastic gradient descent for hybrid quantum-classical optimization. CoRR abs/1910.01155 (2019) - 2018
- [i1]Ryan Sweke, Markus S. Kesselring, Evert P. L. van Nieuwenburg, Jens Eisert:
Reinforcement Learning Decoders for Fault-Tolerant Quantum Computation. CoRR abs/1810.07207 (2018)
Coauthor Index
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