my work.

An overview of my publications can also be found on my Google Scholar profile.

Research#

R. Sweke, E. Recio, S. Jerbi, E. Gil-Fuster, B. Fuller, J. Eisert & J. J. Meyer (2025).
Potential and limitations of random Fourier features for dequantizing quantum machine learning.
Quantum 9, 1640.

Y. Quek, D. Stilck França, S. Khatri, J. J. Meyer & J. Eisert (2024).
Exponentially tighter bounds on limitations of quantum error mitigation.
Nature Physics (Preprint arXiv:2210.11505).

F. Schreiber, J. Eisert & J. J. Meyer (2024).
Tomography of parametrized quantum states.
Preprint arXiv:2407.12916.

E. Recio-Armengol, J. Eisert & J. J. Meyer (2024).
Single-shot quantum machine learning.
Preprint arXiv:2406.13812.

J. A. H. Nielsen, M. Kicinski, T. N. Arge, K. Vijayadharan, J. Foldager, J. Borregaard, J. J. Meyer, J. S. Neergaard-Nielsen, T. Gehring & U. L. Andersen (2023).
Variational quantum algorithm for enhanced continuous variable optical phase sensing.
Preprint arXiv:2312.13870.

F. J. Schreiber, J. Eisert & J. J. Meyer (2023).
Classical surrogates for quantum learning models.
Physical Review Letters 131, 100803 (Preprint arXiv:2206.11740).

J. J. Meyer, S. Khatri, D. Stilck França, J. Eisert & P. Faist (2023).
Quantum metrology in the finite-sample regime.
Preprint arXiv:2307.06370.

J. J. Meyer, M. Mularski, E. Gil-Fuster, A. Anna Mele, F. Arzani, A. Wilms & J. Eisert (2023).
Exploiting symmetry in variational quantum machine learning.
PRX Quantum 4, 010328.

T. Hubregtsen, D. Wierichs, E. Gil-Fuster, P.-J. H. S. Derks, P. K. Faehrmann & J. J. Meyer (2022).
Training quantum embedding kernels on near-term quantum computers.
Physical Review A 106, 042431 (Preprint arXiv:2105.02276).

M. C. Caro, E. Gil-Fuster, J. J. Meyer, J. Eisert & R. Sweke (2021).
Encoding-dependent generalization bounds for parametrized quantum circuits.
Quantum 5, 582.

J. J. Meyer (2021).
Fisher Information in Noisy Intermediate-Scale Quantum Applications.
Quantum 5, 539.

J. J. Meyer, J. Borregaard & J. Eisert (2021).
A variational toolbox for quantum multi-parameter estimation.
npj Quantum Information 7, 89 (Accompanying PennyLane demonstration).

M. Schuld, R. Sweke & J. J. Meyer (2021).
Effect of data encoding on the expressive power of variational quantum-machine-learning models.
Physical Review A 103, 032430 (Preprint arXiv:2008.08605).

R. Sweke, F. Wilde, J. J. Meyer, M. Schuld, P. K. Fährmann, B. Meynard-Piganeau & J. Eisert (2020).
Stochastic gradient descent for hybrid quantum-classical optimization.
Quantum 4, 314.

V. Bergholm, J. Izaac, M. Schuld, C. Gogolin et al.
PennyLane: Automatic differentiation of hybrid quantum-classical computations.
Preprint arXiv:1811.04968.

Other#

P. K. Fährmann, J. J. Meyer & J. Eisert (2023).
Quantencomputer heute und in naher Zukunft: eine realistische Perspektive.
Chancen und Risiken von Quantentechnologien

J. J. Meyer (2021).
Gradients just got more flexible.
Quantum Views 5, 50.