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Paper Citation Record · LEDGER

Unsupervised quantum machine learning for fraud detection

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2208.01203.

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

pith.paper-citation-record.v1
2208.01203 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T12:46:23.450611Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T14:50:04.518945Z

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 471efd0d-405b-48d4-a277-1ae792f3f14a · inbound

Unsupervised Quantum Anomaly Detection on Noisy Quantum Processors cites this paper.

Unsupervised Quantum Anomaly Detection on Noisy Quantum Processors Unsupervised quantum machine learning for fraud detection

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-12T12:46:23.450611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T12:46:23.450611Z digest=sha256:24ae63d9edee818c6ecbf0c3ab9c56630323ce5e3482876a582b23cda6f8805b

Observation 71efa9e5-3263-4cc7-8865-24e9ccef9251 · inbound

Predicting Water Quality using Quantum Machine Learning: The Case of the Umgeni Catchment (U20A) Study Region cites this paper.

Predicting Water Quality using Quantum Machine Learning: The Case of the Umgeni Catchment (U20A) Study Region Unsupervised quantum machine learning for fraud detection

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-12T11:30:37.807396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T11:30:37.807396Z digest=sha256:8f42d9cf83f2ca568074a9234d4ca801762a9ccc7a6047d59cf62cf26b2a2c68

Observation 31094bf8-7193-48dc-bf50-6eb31a9dddf2 · inbound

Comparative Analysis of QNN Architectures for Wind Power Prediction: Feature Maps and Ansatz Configurations cites this paper.

Comparative Analysis of QNN Architectures for Wind Power Prediction: Feature Maps and Ansatz Configurations Unsupervised quantum machine learning for fraud detection

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-07T12:03:32.399112Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:03:32.399112Z digest=sha256:6fafcce3e86c5c82e78dc05856de3dc0cd460f4d4d127e29a4a9b3839923adff

Observation 4b49106f-a016-47be-93af-b686650d4f76 · inbound

Quantum Neural Networks for Wind Energy Forecasting: A Comparative Study of Performance and Scalability with Classical Models cites this paper.

Quantum Neural Networks for Wind Energy Forecasting: A Comparative Study of Performance and Scalability with Classical Models Unsupervised quantum machine learning for fraud detection

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-06T22:03:25.081524Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:03:25.081524Z digest=sha256:96ee40ec5ef1cbb222b02547b34cf2f4192ae98b69dd5f0504d7105d42de8925

Observation f011ea28-c0ab-4490-8e2f-d04d61325b9d · inbound

A Mixture-of-Experts Framework for Practical Hybrid-Quantum Models in Credit Card Fraud Detection cites this paper.

A Mixture-of-Experts Framework for Practical Hybrid-Quantum Models in Credit Card Fraud Detection Unsupervised quantum machine learning for fraud detection

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-05-15T14:50:04.521276Z

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.

source=pdf_text observed=2026-05-15T14:49:32.488934Z digest=sha256:50de496a633436e35a9c5bb4ba30a1ca1abf1220da13e87613a84f94122525aa