Paper Citation Record · LEDGER

Quantum Reservoir Computing for Corrosion Prediction in Aerospace: A Hybrid Approach for Enhanced Material Degradation Forecasting

As of 17 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 1 inbound Pith citation observation for arXiv:2505.22837.

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

pith.paper-citation-record.v1
2505.22837 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:04:14.955322Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T15:08:43.787321Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

17 of 17 outbound references displayed

verified exact0
verified fuzzy15
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 8137daea-079b-4071-aa23-c5b0c0077e33 · outbound

This paper cites Onion echo state networks,.

Quantum Reservoir Computing for Corrosion Prediction in Aerospace: A Hybrid Approach for Enhanced Material Degradation Forecasting Onion echo state networks,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:17.461539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6a92d6e2-49a7-474b-902a-558f1f91d748 · outbound

This paper cites Advanced machine learning techniques for corrosion rate estimation and prediction in industrial cooling water pipelines,.

Quantum Reservoir Computing for Corrosion Prediction in Aerospace: A Hybrid Approach for Enhanced Material Degradation Forecasting Advanced machine learning techniques for corrosion rate estimation and prediction in industrial cooling water pipelines,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:17.342579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f1c48546-1fce-47bf-a5bf-252ac7d61f44 · outbound

This paper cites On the use of machine learning algorithms to predict the corrosion behavior of stainless steels in lactic acid,.

Quantum Reservoir Computing for Corrosion Prediction in Aerospace: A Hybrid Approach for Enhanced Material Degradation Forecasting On the use of machine learning algorithms to predict the corrosion behavior of stainless steels in lactic acid,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:17.207719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c0dd3948-dc50-4cb7-956a-f968b6efc6c7 · outbound

This paper cites Model-based reinforcement corrosion prediction: Contin- uous calibration with bayesian optimization and corrosion wire sensor data,.

Quantum Reservoir Computing for Corrosion Prediction in Aerospace: A Hybrid Approach for Enhanced Material Degradation Forecasting Model-based reinforcement corrosion prediction: Contin- uous calibration with bayesian optimization and corrosion wire sensor data,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:17.043804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6e98594f-5122-4bc2-8149-9efa3d0b73df · outbound

This paper cites A transfer-learning approach for corrosion prediction in pipeline infrastructures,.

Quantum Reservoir Computing for Corrosion Prediction in Aerospace: A Hybrid Approach for Enhanced Material Degradation Forecasting A transfer-learning approach for corrosion prediction in pipeline infrastructures,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:16.889865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6e1c98d7-1b71-4403-b74d-0ef0a70557cd · outbound

This paper cites On the use of machine learning algorithms to predict the corrosion behavior of stainless steels in lactic acid,.

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:16.751271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2758441b-382a-4db9-8bd1-5b7e88595947 · outbound

This paper cites Advanced machine learning techniques for corrosion rate estimation and prediction in industrial cooling water pipelines,.

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:16.608094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 60c3ac8c-730f-40ff-b116-72ec9db7f495 · outbound

This paper cites Prediction of corrosion fatigue crack growth rate in aluminum alloys based on incremental learning strategy,.

Quantum Reservoir Computing for Corrosion Prediction in Aerospace: A Hybrid Approach for Enhanced Material Degradation Forecasting Prediction of corrosion fatigue crack growth rate in aluminum alloys based on incremental learning strategy,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:16.497192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f3582f5f-8e05-4940-8a61-f8b4472ad767 · outbound

This paper cites Time-series forecasting using continuous variables-based quantum neural networks,.

Quantum Reservoir Computing for Corrosion Prediction in Aerospace: A Hybrid Approach for Enhanced Material Degradation Forecasting Time-series forecasting using continuous variables-based quantum neural networks,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:16.343180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 655ac1f2-3af6-47cf-b5fc-41c68946ab04 · outbound

This paper cites Quantum multi-agent reinforcement learning for aerial ad-hoc networks,.

Quantum Reservoir Computing for Corrosion Prediction in Aerospace: A Hybrid Approach for Enhanced Material Degradation Forecasting Quantum multi-agent reinforcement learning for aerial ad-hoc networks,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:16.155932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9fc65749-ef06-487b-aee0-fedfedf33c2f · outbound

This paper cites Robust Quantum Reservoir Computing for Molecular Property Prediction.

Quantum Reservoir Computing for Corrosion Prediction in Aerospace: A Hybrid Approach for Enhanced Material Degradation Forecasting Robust Quantum Reservoir Computing for Molecular Property Prediction

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T13:04:14.543864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43ca6649-acb7-4ae4-91ce-51babb0d6637 · outbound

This paper cites Onion echo state networks: A preliminary analysis of dynamics,.

Quantum Reservoir Computing for Corrosion Prediction in Aerospace: A Hybrid Approach for Enhanced Material Degradation Forecasting Onion echo state networks: A preliminary analysis of dynamics,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:15.998976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1e1a31bb-2608-452a-908b-27d66331c649 · outbound

This paper cites Non-unital noise in a superconducting quantum computer as a computational resource for reservoir computing,.

Quantum Reservoir Computing for Corrosion Prediction in Aerospace: A Hybrid Approach for Enhanced Material Degradation Forecasting Non-unital noise in a superconducting quantum computer as a computational resource for reservoir computing,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:15.878020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 66fddbfe-1608-42b5-833a-436c90ecfd22 · outbound

This paper cites Feedback-driven quantum reservoir computing for time-series analysis,.

Quantum Reservoir Computing for Corrosion Prediction in Aerospace: A Hybrid Approach for Enhanced Material Degradation Forecasting Feedback-driven quantum reservoir computing for time-series analysis,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:15.698441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5165191a-b4fb-4446-a581-1afbd077081b · outbound

This paper cites an unresolved cited work.

Quantum Reservoir Computing for Corrosion Prediction in Aerospace: A Hybrid Approach for Enhanced Material Degradation Forecasting Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:04:15.470506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 75e7e29b-154b-449e-a49d-7dd1e63ef6bc · outbound

This paper cites Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets,.

Quantum Reservoir Computing for Corrosion Prediction in Aerospace: A Hybrid Approach for Enhanced Material Degradation Forecasting Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:15.300552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d7b2c44c-5268-44ed-8c39-937cf52377dd · outbound

This paper cites Large-scale quantum reservoir learning with an analog quantum computer,.

Quantum Reservoir Computing for Corrosion Prediction in Aerospace: A Hybrid Approach for Enhanced Material Degradation Forecasting Large-scale quantum reservoir learning with an analog quantum computer,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:04:15.157790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

Observation 9553a674-ae62-4f02-9f6f-812ed4931835 · inbound

Quantum Reservoir Computing: Recent Advances and Future Directions cites this paper.

Quantum Reservoir Computing: Recent Advances and Future Directions Quantum Reservoir Computing for Corrosion Prediction in Aerospace: A Hybrid Approach for Enhanced Material Degradation Forecasting

Reference 127

Resolution: verified exact; local_arxiv, observed 2026-08-01T15:13:31.011813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.