The DOI matches its Crossref record.
Green Function Monte Carlo with Stochastic Reconfiguration
Open accessAbstract
A new method for the stabilization of the sign problem in the Green function Monte Carlo technique is proposed. The method is devised for real lattice Hamiltonians and is based on an iterative ``stochastic reconfiguration'' scheme which introduces some bias but allows a stable simulation with constant sign. The systematic reduction of this bias is possible in principle. The method is applied to the frustrated ${J}_{1}{\ensuremath{-}J}_{2}$ Heisenberg model, and tested against exact diagonalization data. Evidence of a finite spin gap for ${J}_{2}{/J}_{1}>\ensuremath{\sim}0.4$ is found in the thermodynamic limit.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Quantum Monte Carlo Approaches for Correlated Systems
Becca, F., & Sorella, S. (2017) · doi:10.1017/9781316417041 · Cited by 523
Self-citationNetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems
Vicentini, F., Hofmann, D., Szabó, A., Wu, D., Roth, C., Giuliani, C., Pescia, G., Nys, J., Vargas-Calderón, V., Astrakhantsev, N., et al. (2022) · SciPost Physics Codebases · doi:10.21468/scipostphyscodeb.7 · Cited by 135
Classical variational simulation of the Quantum Approximate Optimization Algorithm
Medvidović, M., & Carleo, G. (2021) · npj Quantum Information · doi:10.1038/s41534-021-00440-z · Cited by 79
Variational benchmarks for quantum many-body problems
Wu, D., Rossi, R., Vicentini, F., Astrakhantsev, N., Becca, F., Cao, X., Carrasquilla, J., Ferrari, F., Georges, A., Hibat-Allah, M., et al. (2024) · Science · doi:10.1126/science.adg9774 · Cited by 79
Self-citationDirect sampling of projected entangled-pair states
Vieijra, T., Haegeman, J., Verstraete, F., & Vanderstraeten, L. (2021) · Physical Review B · doi:10.1103/physrevb.104.235141 · Cited by 37
Metadata
- Authors
- Sorella, S.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 1998-05-18
- Volume
- 80
- Issue
- 20
- Pages
- 4558-4561
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2079488937
- Cited by
- 269
- References
- 0
- FWCI
- 6.13
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Green Function Monte Carlo with Stochastic Reconfiguration. https://app.cochat.ai/papers/10.1103/physrevlett.80.4558
- The paper
- Sorella, S. (1998). Green Function Monte Carlo with Stochastic Reconfiguration. Physical Review Letters. https://doi.org/10.1103/physrevlett.80.4558
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.