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PreprintCC BY 4.0arXiv

Variational Block Correlated Coupled Cluster Monte Carlo

  • Hunter Brodie
  • Alexander Shilcusky
  • Adrian E. Feiguin
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Abstract

Motivated by the recent success of quantum neural states, we present a variational implementation of the coupled cluster method suitable for quantum Monte Carlo simulations. We introduce a Young tableaux representation of the wave-function coefficients and their derivatives that substantially reduces the computational complexity of the variational optimization. While the algorithm is generic, we illustrate its implementation with an application to the Heisenberg model in one and two dimensions, using the block correlated coupled cluster ansatz (BCCC) with doubles and triples. Convergence is greatly improved by means of importance sampling based on a fictitious temperature. The resulting approach achieves competitive accuracy while providing a scalable framework for variational coupled-cluster calculations.

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Cite

BibTeXRIS
BibTeX
@misc{brodie2026variational,
  title = {{Variational Block Correlated Coupled Cluster Monte Carlo}},
  author = {Hunter Brodie and Alexander Shilcusky and Adrian E. Feiguin},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.26646},
  eprint = {2610.26646},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.26646},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Variational Block Correlated Coupled Cluster Monte Carlo
AU  - Hunter Brodie
AU  - Alexander Shilcusky
AU  - Adrian E. Feiguin
PY  - 2026
DA  - 2026/10/19
PB  - arXiv
DO  - 10.48550/arxiv.2610.26646
UR  - https://arxiv.org/abs/2610.26646
AB  - Motivated by the recent success of quantum neural states, we present a variational implementation of the coupled cluster method suitable for quantum Monte Carlo simulations. We introduce a Young tableaux representation of the wave-function coefficients and their derivatives that substantially reduces the computational complexity of the variational optimization. While the algorithm is generic, we illustrate its implementation with an application to the Heisenberg model in one and two dimensions, using the block correlated coupled cluster ansatz (BCCC) with doubles and triples. Convergence is greatly improved by means of importance sampling based on a fictitious temperature. The resulting approach achieves competitive accuracy while providing a scalable framework for variational coupled-cluster calculations.
ER  -