Expressive power of tensor-network factorizations for probabilistic modeling. - Ivan Glasser; Ryan Sweke; Nicola Pancotti; Jens Eisert; J. Ignacio Cirac (NeurIPS, 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)

Tensor network approaches for learning non-linear dynamical laws. - Alex Goeßmann; M. Götte; Ingo Roth; Ryan Sweke; Gitta Kutyniok; Jens Eisert (CoRR, 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)

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)

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)

Encoding-dependent generalization bounds for parametrized quantum circuits. - Matthias C. Caro; Elies Gil-Fuster; Johannes Jakob Meyer; Jens Eisert; Ryan Sweke (Quantum, 2021)

On the Quantum versus Classical Learnability of Discrete Distributions. - Ryan Sweke; Jean-Pierre Seifert; Dominik Hangleiter; Jens Eisert (Quantum, 2021)

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)

Scalably learning quantum many-body Hamiltonians from dynamical data. - Frederik Wilde; Augustine Kshetrimayum; Ingo Roth; Dominik Hangleiter; Ryan Sweke; Jens Eisert (CoRR, 2022)

A super-polynomial quantum-classical separation for density modelling. - Niklas Pirnay; Ryan Sweke; Jens Eisert; Jean-Pierre Seifert (CoRR, 2022)

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)

Classical Verification of Quantum Learning. - Matthias C. Caro; Marcel Hinsche; Marios Ioannou; Alexander Nietner; Ryan Sweke (ITCS, 2024)

Interactive proofs for verifying (quantum) learning and testing. - Matthias C. Caro; Jens Eisert; Marcel Hinsche; Marios Ioannou; Alexander Nietner; Ryan Sweke (CoRR, 2024)

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 (Quantum, 2025)

Potential and limitations of random Fourier features for dequantizing quantum machine learning. - Ryan Sweke; Erik Recio-Armengol; Sofiène Jerbi; Elies Gil-Fuster; Bryce Fuller; Jens Eisert; Johannes Jakob Meyer (Quantum, 2025)

Wavefunction Flows: Efficient Quantum Simulation of Continuous Flow Models. - David Layden; Ryan Sweke; Vojtech Havlícek; Anirban Chowdhury; Kirill Neklyudov (CoRR, 2025)