Paper Citation Record · LEDGER

Comparative Analysis of QNN Architectures for Wind Power Prediction: Feature Maps and Ansatz Configurations

As of 8 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 1 inbound Pith citation observation for arXiv:2506.14795.

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

pith.paper-citation-record.v1
2506.14795 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:03:35.262281Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:03:28.313339Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T22:03:31.343649Z

Reference resolution

19 of 19 outbound references displayed

verified exact0
verified fuzzy14
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

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

This paper cites Unsupervised quantum machine learning for fraud detection.

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

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0eacc3b6-2be9-4998-a116-45e51cb218f5 · outbound

This paper cites Quantum machine learning for anomaly detection in consumer electronics,.

Comparative Analysis of QNN Architectures for Wind Power Prediction: Feature Maps and Ansatz Configurations Quantum machine learning for anomaly detection in consumer electronics,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:03:39.708714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 009c482a-d767-4d3e-a134-540f7011b9d2 · outbound

This paper cites Anomaly detection for real-world cyber-physical security using quantum hybrid support vector machines,.

Comparative Analysis of QNN Architectures for Wind Power Prediction: Feature Maps and Ansatz Configurations Anomaly detection for real-world cyber-physical security using quantum hybrid support vector machines,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:03:39.451405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ef39d5c6-bf71-4a08-815a-f0c7825f447b · outbound

This paper cites A robust hybrid classical and quantum model for short-term wind speed forecasting,.

Comparative Analysis of QNN Architectures for Wind Power Prediction: Feature Maps and Ansatz Configurations A robust hybrid classical and quantum model for short-term wind speed forecasting,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T12:03:32.885939Z

Source-reported events for the cited work

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Observation 12bd12be-5677-49a1-b208-e8b0f5bec257 · outbound

This paper cites Analysis of quantum machine learning algorithms in noisy channels for classification tasks in the iot extreme environment,.

Comparative Analysis of QNN Architectures for Wind Power Prediction: Feature Maps and Ansatz Configurations Analysis of quantum machine learning algorithms in noisy channels for classification tasks in the iot extreme environment,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:03:39.158282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0f40275b-5365-4e05-a655-a08b13bda940 · outbound

This paper cites Quantum long short-term memory (qlstm) vs. classical lstm in time series forecasting: a comparative study in solar power forecasting,.

Comparative Analysis of QNN Architectures for Wind Power Prediction: Feature Maps and Ansatz Configurations Quantum long short-term memory (qlstm) vs. classical lstm in time series forecasting: a comparative study in solar power forecasting,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:03:38.855852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 347810a5-9034-4c1f-b04d-4bb149cc81bc · outbound

This paper cites Extended abstract: Quantum-accelerated transient stability assessment for power systems,.

Comparative Analysis of QNN Architectures for Wind Power Prediction: Feature Maps and Ansatz Configurations Extended abstract: Quantum-accelerated transient stability assessment for power systems,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:03:38.519173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dd0702a9-90fe-49e8-b77d-76e7f52b3da2 · outbound

This paper cites Quantum computing based hybrid deep learning for fault diagnosis in electrical power systems,.

Comparative Analysis of QNN Architectures for Wind Power Prediction: Feature Maps and Ansatz Configurations Quantum computing based hybrid deep learning for fault diagnosis in electrical power systems,

Reference 8

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Source-reported events for the cited work

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Observation aa0dfe78-b214-4303-aef8-b360635fea30 · outbound

This paper cites Quantum computing for energy systems optimization: Challenges and opportunities,.

Comparative Analysis of QNN Architectures for Wind Power Prediction: Feature Maps and Ansatz Configurations Quantum computing for energy systems optimization: Challenges and opportunities,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:03:38.226753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b8fec807-1a3f-4f8b-b6fb-faff53587353 · outbound

This paper cites Neuroquman: quantum neural network-based consumer reaction time demand response predictive management,.

Comparative Analysis of QNN Architectures for Wind Power Prediction: Feature Maps and Ansatz Configurations Neuroquman: quantum neural network-based consumer reaction time demand response predictive management,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:03:37.925614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 368acf04-4899-4b4f-b1c1-39762a3daf8c · outbound

This paper cites Prediction of solar irradiance one hour ahead based on quantum long short-term memory network,.

Comparative Analysis of QNN Architectures for Wind Power Prediction: Feature Maps and Ansatz Configurations Prediction of solar irradiance one hour ahead based on quantum long short-term memory network,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:03:37.584481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 74294f21-e217-463a-8624-c62ff12526c3 · outbound

This paper cites Noise-resilient quantum machine learning for stability assessment of power systems,.

Comparative Analysis of QNN Architectures for Wind Power Prediction: Feature Maps and Ansatz Configurations Noise-resilient quantum machine learning for stability assessment of power systems,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:03:37.252091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2ce65e96-cf63-4715-8322-bb4bc4bd4fad · outbound

This paper cites Quantum renewable scenario genera- tion,.

Comparative Analysis of QNN Architectures for Wind Power Prediction: Feature Maps and Ansatz Configurations Quantum renewable scenario genera- tion,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:03:36.979368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bf109590-7ab6-41a6-aad7-4370d729968d · outbound

This paper cites Quantum computing approach to smart grid stability forecasting,.

Comparative Analysis of QNN Architectures for Wind Power Prediction: Feature Maps and Ansatz Configurations Quantum computing approach to smart grid stability forecasting,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:03:36.603057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2749829e-afe0-4b2a-bb32-469d41ae2649 · outbound

This paper cites A hybrid quantum-classical machine learning approach to offshore wind farm power forecasting,.

Comparative Analysis of QNN Architectures for Wind Power Prediction: Feature Maps and Ansatz Configurations A hybrid quantum-classical machine learning approach to offshore wind farm power forecasting,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:03:36.331966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 28bc0d13-b628-4a33-9940-b894309b2234 · outbound

This paper cites Database on wind character- istics,.

Comparative Analysis of QNN Architectures for Wind Power Prediction: Feature Maps and Ansatz Configurations Database on wind character- istics,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:03:36.049610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f1697ccf-dea6-4822-9f39-b3d7be748ac4 · outbound

This paper cites Performance comparison of different machine learning algorithms on the prediction of wind turbine power generation,.

Comparative Analysis of QNN Architectures for Wind Power Prediction: Feature Maps and Ansatz Configurations Performance comparison of different machine learning algorithms on the prediction of wind turbine power generation,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:03:35.764758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 91dead76-73e4-4316-bcd0-d6ccd4cc7fae · outbound

This paper cites Quantum computing with Qiskit,.

Comparative Analysis of QNN Architectures for Wind Power Prediction: Feature Maps and Ansatz Configurations Quantum computing with Qiskit,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T12:03:35.154627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38ab8033-9869-41b9-81a0-85d33083721e · outbound

This paper cites Qiskit Machine Learning: an open-source library for quantum machine learning tasks at scale on quantum hardware and classical simulators.

Comparative Analysis of QNN Architectures for Wind Power Prediction: Feature Maps and Ansatz Configurations Qiskit Machine Learning: an open-source library for quantum machine learning tasks at scale on quantum hardware and classical simulators

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T12:03:35.262281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 36bdb7df-7cb0-42d1-b271-6a707474aad4 · 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 Comparative Analysis of QNN Architectures for Wind Power Prediction: Feature Maps and Ansatz Configurations

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:03:31.462927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.