Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Concept learning of parameterized quantum models from limited measurements
As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2408.05116.
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 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T15:25:30.715380Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T05:50:41.264970Z
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 556eccac-ead4-4c34-b45c-6ae721c17f23 · inbound
Addressing the Readout Problem in Quantum Differential Equation Algorithms with Quantum Scientific Machine Learning cites this paper.
Addressing the Readout Problem in Quantum Differential Equation Algorithms with Quantum Scientific Machine Learning Concept learning of parameterized quantum models from limited measurements
Reference 106
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T15:25:30.715380Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 51858166-583f-460e-95f9-33a68d7462ed · inbound
Demonstration of Efficient Predictive Surrogates for Large-scale Quantum Processors cites this paper.
Demonstration of Efficient Predictive Surrogates for Large-scale Quantum Processors Concept learning of parameterized quantum models from limited measurements
Reference 106
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:59:11.303444Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d22d97f8-78d6-4061-9b7f-8ec7a1679e56 · inbound
Artificial intelligence for representing and characterizing quantum systems cites this paper.
Artificial intelligence for representing and characterizing quantum systems Concept learning of parameterized quantum models from limited measurements
Reference 97
- Resolution
- verified exact
- local_arxiv, observed 2026-08-05T05:50:41.269615Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.