my work.

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

Research#

J. J. Meyer, J. Rizzo, A. Raza, L. Leone, S. Jerbi & J. Eisert (2026)
The computational two-way quantum capacity.
Preprint arXiv:2601.15393.

Z. Huang, J. J. Meyer, T. Nuradha & M. M. Wilde (2025)
Query complexities of quantum channel discrimination and estimation: A unified approach.
Preprint arXiv:2511.10832.

G. Karaiskos, D. Rudolph, J. J. Meyer, J. Eisert & S. Gharibian (2025)
How hard is it to verify a classical shadow?
Preprint arXiv:2510.08515.

J. J. Meyer, A. Raza, J. Rizzo, L. Leone, S. Jerbi & J. Eisert (2025)
Computational Relative Entropy.
Preprint arXiv:2509.20472.

J. J. Meyer, S. Khatri, D. Stilck França, J. Eisert & P. Faist (2025).
Quantum metrology in the finite-sample regime.
PRX Quantum 6, 030336.

F. Schreiber, J. Eisert & J. J. Meyer (2025).
Tomography of parametrized quantum states.
PRX Quantum 6, 020346.

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 (2025).
Variational quantum algorithm for enhanced continuous variable optical phase sensing.
npj Quantum Information 11, 70.

E. Recio-Armengol, J. Eisert & J. J. Meyer (2025).
Single-shot quantum machine learning.
Physical Review A 111, 042420.

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 20, 1648–1658 (Preprint arXiv:2210.11505).

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, 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.