my work.

 

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

Papers#

Schuld, M., Sweke, R., & Meyer, J. J. (2020). The effect of data encoding on the expressive power of variational quantum machine learning models. 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.