Paper Citation Record · LEDGER

Efficient optimization of neural network backflow for ab-initio quantum chemistry

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2502.18843.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.18843 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T15:33:58.269357Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 9081054d-f197-4087-b7b9-63d726753878 · inbound

Looking elsewhere: improving variational Monte Carlo gradients by importance sampling cites this paper.

Looking elsewhere: improving variational Monte Carlo gradients by importance sampling Efficient optimization of neural network backflow for ab-initio quantum chemistry

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T19:37:26.461159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9999a402-4d9a-4cc6-9a16-fe385176ac08 · inbound

Bayesian perspectives for quantum states and application to ab initio quantum chemistry cites this paper.

Bayesian perspectives for quantum states and application to ab initio quantum chemistry Efficient optimization of neural network backflow for ab-initio quantum chemistry

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-05T14:05:27.305734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9e2559e-abfb-4771-a543-54e25d16f568 · inbound

Absorbing Many-Body Correlations into Core-Optimized Orbitals cites this paper.

Absorbing Many-Body Correlations into Core-Optimized Orbitals Efficient optimization of neural network backflow for ab-initio quantum chemistry

Reference 149

Resolution: verified exact; arxiv_id, observed 2026-05-25T05:36:38.859821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7820f43e-6387-4546-bcac-b382478886ba · inbound

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier cites this paper.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Efficient optimization of neural network backflow for ab-initio quantum chemistry

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.269357Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.