my work.

 

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

Papers#

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

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

Meyer, J. J., Borregaard, J., & Eisert, J. (2020). A variational toolbox for quantum multi-parameter estimation. arXiv preprint arXiv:2006.06303. I also made an accompanying PennyLane demonstration.

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

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

Perspective Articles#

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