# Colloquium: Leaving the physical corner: Tensor network methods augmented by mode transformations in molecular quantum chemistry and condensed matter physics

- Date
- Mar 9, 2020
- Time
- 4:30 PM - 5:30 PM
- Speaker
- Prof. Dr. Jens Eisert
- Affiliation
- Freie Universität Berlin
- Series
- MPI-PKS Kolloquium
- Language
- en
- Main Topic
- Physik
- Other Topics
- Physik
- Description
- Tensor network methods have become workhorses in the study of strongly correlated systems in condensed matter physics and increasingly also in the context of molecular systems. While their variational character provides results of remarkable accuracy, the capture quantum states featuring low entanglement well, a set of states often referred to as the 'physical corner'. While this physical describes ground states of local Hamiltonian problems from the condensed matter context well, the same cannot be said for problems involving molecules in quantum chemistry or systems undergoing time evolution. In this talk I will provide an overview over tensor network methods augmented by fermionic mode transformations in the context of molecular problems [1], condensed-matter simulations [2], and time evolution [3]. If time allows, I might briefly mention two new applications of tensor networks in being a design principle for building quantum devices [4] and in machine learning [5,6].

[1] Fermionic orbital optimisation in tensor network states

C. Krumnow, L. Veis, Ö. Legeza, J. Eisert

Phys. Rev. Lett. 117, 210402 (2016)

[2] Towards overcoming the entanglement barrier when simulating long-time evolution

C. Krumnow, J. Eisert, Ö. Legeza

arXiv:1904.11999

[3] Dimension reduction with mode transformations: Simulating two-dimensional fermionic condensed matter systems

C. Krumnow, L. Veis, J. Eisert, Ö. Legeza

arXiv:1906.00205

[4] Simulating topological tensor networks with Majorana qubits

C. Wille, R. Egger, J. Eisert, A. Altland

Phys. Rev. B 99, 115117 (2019)

[5] 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

arXiv:1907.03741, NeurIPS (2019)

[6] Tensor network approaches for learning non-linear dynamical laws

A. Goessmann, M. Goette, I. Roth, G. Kutyniok, J. Eisert, R. Sweke

Submitted to ICML2020 (2020)

*Last modified: Feb 21, 2020, 12:10:07 AM*

## Location

**Max-Planck-Institut für Physik komplexer Systeme**Nöthnitzer Straße3801187Dresden

- Phone
- + 49 (0)351 871 0
- MPI-PKS
- Homepage
- http://www.mpipks-dresden.mpg.de

## Organizer

**Max-Planck-Institut für Physik komplexer Systeme**Nöthnitzer Straße3801187Dresden

- Phone
- + 49 (0)351 871 0
- MPI-PKS
- Homepage
- http://www.mpipks-dresden.mpg.de

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