Ryan Sweke

According to our database, Ryan Sweke authored at least 14 papers between 2019 and 2024.

Collaborative distances:

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

2024

Classical Verification of Quantum Learning.

Matthias C. Caro, Marcel Hinsche, Marios Ioannou, Alexander Nietner, Ryan Sweke

Proceedings of the 15th Innovations in Theoretical Computer Science Conference, 2024

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

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

2022

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

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

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

2021

On the Quantum versus Classical Learnability of Discrete Distributions.

Ryan Sweke, Jean-Pierre Seifert, Dominik Hangleiter, 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

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

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

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