my work.

 

An overview of my publications along with citation metrics can also be found on my Google Scholar profile.

Papers#

R. Sweke, E. Recio, S. Jerbi, E. Gil-Fuster, B. Fuller, J. Eisert & J. J. Meyer (2023).
Potential and limitations of random Fourier features for dequantizing quantum machine learning.
Preprint arXiv:2309.11647.

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.

Y. Quek, D. Stilck França, S. Khatri, J. J. Meyer & J. Eisert (2022).
Exponentially tighter bounds on limitations of quantum error mitigation.
Preprint arXiv:2210.11505.

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, M. S. Alam, S. Ahmed, J. M. Arrazola, C. Blank, A. Delgado, S. Jahangiri, K. McKiernan, J. J. Meyer, Z. Niu, A. Száva & N. Killoran (2018).
PennyLane: Automatic differentiation of hybrid quantum-classical computations.
Preprint arXiv:1811.04968.

Perspective Articles#

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