Ph

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 SystemeNöthnitzer Straße3801187Dresden
Phone
+ 49 (0)351 871 0
E-Mail
MPI-PKS
Homepage
http://www.mpipks-dresden.mpg.de

Organizer

Max-Planck-Institut für Physik komplexer SystemeNöthnitzer Straße3801187Dresden
Phone
+ 49 (0)351 871 0
E-Mail
MPI-PKS
Homepage
http://www.mpipks-dresden.mpg.de
Scan this code with your smartphone and get directly this event in your calendar. Increase the image size by clicking on the QR-Code if you have problems to scan it.
  • BiBiology
  • ChChemistry
  • CiCivil Eng., Architecture
  • CoComputer Science
  • EcEconomics
  • ElElectrical and Computer Eng.
  • EnEnvironmental Sciences
  • LaLaw
  • CuLinguistics, Literature and Culture
  • MtMaterials
  • MaMathematics
  • McMechanical Engineering
  • MeMedicine
  • PhPhysics
  • PsPsychology
  • SoSociety, Philosophy, Education
  • SpSpin-off/Transfer
  • TrTraffic
  • TgTraining
  • WlWelcome