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PreprintCC BYChemRxiv

Localized Sequential Backflow Ansatz for Scalable Neural Quantum States

  • Dylan Liu-Walter
Open at sourcePDFDOICite

Abstract

Neural Quantum State Architectures have been shown to have high accuracy on a variety of molecular and lattice systems, often outperforming traditional methods. This work replaces the all-to-all paradigm of current neural quantum states with a localized recurrent sequential backflow mechanism. Results have empirically exhibited approximately quadratic wall-clock scaling over the system sizes tested, namely the hydrogen chain with atom with length of H 10 to H 400 , suggesting a viable path towards a scalable Neural Quantum State.

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Cite

BibTeXRIS
BibTeX
@misc{liuwalter2026localized,
  title = {{Localized Sequential Backflow Ansatz for Scalable Neural Quantum States}},
  author = {Dylan Liu-Walter},
  howpublished = {ChemRxiv},
  year = {2026},
  month = oct,
  doi = {10.26434/chemrxiv.15009752/v1},
  url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009752/v1},
  note = {CC BY},
}
RIS
TY  - UNPB
TI  - Localized Sequential Backflow Ansatz for Scalable Neural Quantum States
AU  - Dylan Liu-Walter
PY  - 2026
DA  - 2026/10/01
PB  - ChemRxiv
DO  - 10.26434/chemrxiv.15009752/v1
UR  - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009752/v1
AB  - Neural Quantum State Architectures have been shown to have high accuracy on a variety of molecular and lattice systems, often outperforming traditional methods. This work replaces the all-to-all paradigm of current neural quantum states with a localized recurrent sequential backflow mechanism. Results have empirically exhibited approximately quadratic wall-clock scaling over the system sizes tested, namely the hydrogen chain with atom with length of H 10 to H 400 , suggesting a viable path towards a scalable Neural Quantum State.
ER  -