Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Machine learning-based compression of quantum many body physics: PCA and autoencoder representation of the vertex function
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2403.15372.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T18:37:48.334733Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-08T18:37:49.325115Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 8814f25e-fd6d-4429-971f-ce1456e51e60 · inbound
Data-driven Low-rank Approximation for Electron-hole Kernel and Acceleration of Time-dependent GW Calculations cites this paper.
Data-driven Low-rank Approximation for Electron-hole Kernel and Acceleration of Time-dependent GW Calculations Machine learning-based compression of quantum many body physics: PCA and autoencoder representation of the vertex function
Reference 32
- Resolution
- verified exact
- local_arxiv, observed 2026-08-08T18:37:49.330242Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 4c84a0d0-92de-4357-8fc5-1dcf2fd6a4d0 · inbound
Learning Minimal Representations of Fermionic Ground States cites this paper.
Learning Minimal Representations of Fermionic Ground States Machine learning-based compression of quantum many body physics: PCA and autoencoder representation of the vertex function
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T16:50:10.247735Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.