Adiabatic transport of neural network quantum states

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.

Quantum Physics (quant-ph); Disordered Systems and Neural Networks (cond-mat.dis-nn)

arXiv:2510.15030 [quant-ph]

(or arXiv:2510.15030v2 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2510.15030 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1103/khy4-5v68 Focus to learn more DOI(s) linking to related resources

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