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  • Letter
  • Open Access

Correlation-enhanced neural networks as interpretable variational quantum states

Agnes Valenti1, Eliska Greplova1,2, Netanel H. Lindner3, and Sebastian D. Huber1

  • 1Institute for Theoretical Physics, ETH Zurich, CH-8093 Zurich, Switzerland
  • 2Kavli Institute of Nanoscience, Delft University of Technology, NL-2600 GA Delft, The Netherlands
  • 3Physics Department, Technion, Haifa 3200003, Israel

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Phys. Rev. Research 4, L012010 – Published 31 January, 2022

DOI: https://doi.org/10.1103/PhysRevResearch.4.L012010

Abstract

Variational methods have proven to be excellent tools to approximate the ground states of complex many-body Hamiltonians. Generic tools such as neural networks are extremely powerful, but their parameters are not necessarily physically motivated. Thus, an efficient parametrization of the wave function can become challenging. In this Letter we introduce a neural-network-based variational ansatz that retains the flexibility of these generic methods while allowing for a tunability with respect to the relevant correlations governing the physics of the system. We illustrate the success of this approach on topological, long-range correlated, and frustrated models. Additionally, we introduce compatible variational optimization methods for the exploration of low-lying excited states without symmetries that preserve the interpretability of the ansatz.

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Phys. Rev. Research 4, L012010– Published 31 January, 2022
  • Received 10 March 2021
  • Revised 13 January 2022
  • Accepted 14 January 2022
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