my work.

 

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

Papers#

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

T. Hubregtsen, D. Wierichs, E. Gil-Fuster, P.-J. H. S. Derks, P. K. Faehrmann & J. J. Meyer (2021).
Training Quantum Embedding Kernels on Near-Term Quantum Computers.
Preprint arXiv:2105.02276.

J. J. Meyer (2021).
Fisher Information in Noisy Intermediate-Scale Quantum Applications.
Preprint arXiv:2103.15191.

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#

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