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Paper Citation Record · LEDGER

Quantum Machine Learning for Radio Astronomy

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2112.02655.

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

pith.paper-citation-record.v1
2112.02655 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:42:21.329427Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T03:25:57.571346Z

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 f2be16b6-0914-4b72-9dcd-93e55f014600 · inbound

Unlocking the hidden potential of pulsar astronomy cites this paper.

Unlocking the hidden potential of pulsar astronomy Quantum Machine Learning for Radio Astronomy

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-07T05:42:21.329427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T05:42:21.329427Z digest=sha256:28f04dd76554c645171b3e72b51e9f931c865fc3d05c17c8f59f481b35e2ebf3

Observation 63673450-0b37-4723-97cd-c6b4c6f3708a · inbound

Hybrid Quantum-Classical Logistic Regression for Calibrated Classification of Pulsar Candidates cites this paper.

Hybrid Quantum-Classical Logistic Regression for Calibrated Classification of Pulsar Candidates Quantum Machine Learning for Radio Astronomy

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-05-11T03:25:57.575678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-11T03:21:59.625497Z digest=sha256:3375a50ec2ee3a3467fe79286d941662edeefe358e45a8c91bcd85d572af5792