**Project Heads**

*Jens Eisert, Klaus-Robert Müller*

**Project Members**

Jens Eisert (FU), Frederik Wilde (FU), Klaus-Robert Müller (TU)

**Project Duration**

01.03.2019 – 28.02.2022

**Located at**

FU Berlin

**Project Webpages**

**Selected Publications
**

- Scalably learning quantum many-body Hamiltonians from dynamical data, F. Wilde, A. Kshetrimayum, R. Sweke, I. Roth, J. Eisert, (in preparation)
- Single-component gradient rules for variational quantum algorithms, T. Hubregtsen, F. Wilde, S. Qasim, J. Eisert, arxiv:2106.01388 (2021)
- Stochastic gradient descent for hybrid quantum-classical optimization, R. Sweke, F. Wilde, J. Meyer, M. Schuld, P. K. Fährmann, B. Meynard-Piganeau, J. Eisert,
*Quantum*4, 314 (2020).

- Expressive power of tensor-network factorizations for probabilistic modeling, with applications from hidden Markov models to quantum machine learning, I. Glasser, R. Sweke, N. Pancotti, J. Eisert, J. I. Cirac, Advances in Neural Information Processing Systems 32,
*Proceedings of the NeurIPS 2019 Conference*(2019).

- Tensor network approaches for learning non-linear dynamical laws, A. Goeßmann, M. Götte, I. Roth, R. Sweke, G. Kutyniok, J. Eisert, arXiv:2002.12388 (2020),
*Proceedings of the NeurIPS 2020 Conference*(2020).

- Quantum certification and benchmarking, J. Eisert, D. Hangleiter, N. Walk, I. Roth, D. Markham, R. Parekh, U. Chabaud, E. Kashefi, arXiv:1910.06343,
*Nature Reviews*Phys. 2, 382-390 (2020).

- A variational toolbox for quantum multi-parameter estimation, J. Jakob Meyer, J. Borregaard, J. Eisert,
*Nature Partner Journal Quantum Information*7, 89 (2021). -
The effect of data encoding on the expressive power of variational quantum machine learning models, M. Schuld, R. Sweke, J. J. Meyer,
*Physical Review A*103, 032430 (2021).

- Unifying machine learning and quantum chemistry – a deep neural network for molecular wavefunctions, K. T. Schütt, M. Gastegger, A. Tkatchenko, K. -R. Müller, R. J. Maurer,
*Nature Communication*10, 5024 (2019).

**Selected Pictures
**

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