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Adiabatic Transport of Neural Network Quantum States

Matija Medvidović1,*, Alev Orfi2,3, Juan Carrasquilla1, and Dries Sels2,4

  • 1Institute for Theoretical Physics, ETH Zürich, CH-8093 Zürich, Switzerland
  • 2Center for Computational Quantum Physics, Flatiron Institute, 162 5th Avenue, New York, New York 10010, USA
  • 3Center for Quantum Phenomena, Department of Physics, New York University, 726 Broadway, New York, New York 10003, USA
  • 4Department of Physics, Boston University, 590 Commonwealth Ave., Boston, Massachusetts 02215, USA

  • *Contact author: mmedvidovic@ethz.ch
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PRX Intelligence 1, 013020 – Published 15 September, 2026

DOI: https://doi.org/10.1103/khy4-5v68

Abstract

Variational methods have offered controllable and powerful tools for capturing many-body quantum physics for decades. The recent introduction of expressive neural network quantum states has enabled the accurate representation of a broad class of complex wavefunctions for many Hamiltonians of interest. We introduce a first-principles method for building neural network representations of many-body excited states by adiabatically continuing eigenstates of simple Hamiltonians into the strongly correlated regime. With controlled access to the full many-body gap, we obtain accurate estimates of critical exponents. Successive eigenstate estimates can be run entirely in parallel, enabling precise targeting of excited-state properties without reference to the rest of the spectrum, opening the door to large-scale numerical investigations of universal properties of entire phases of matter.

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Outline

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PRX Intelligence 1, 013020– Published 15 September, 2026
  • Received 21 October 2025
  • Revised 21 May 2026
  • Accepted 29 July 2026
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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