Work

Adiabatic Transport of Neural Network Quantum States

Year: 2026

Type: article

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.

Source: PRX Intelligence

DOI: 10.1103/khy4-5v68

Authors: Matija Medvidović, Alev Orfi, Juan Carrasquilla, Dries Sels

Institutions: ETH Zurich, Flatiron Institute, New York University, Boston University

Language: English

Topic: Quantum many-body systems

Subfield: Atomic and Molecular Physics, and Optics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: hybrid

Funders: Simons Foundation, Flatiron Health, Air Force Office of Scientific Research

Award: G4165901653