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Paper Citation Record · LEDGER

Investigating the Effect of Noise on the Training Performance of Hybrid Quantum Neural Networks

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2402.08523.

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

pith.paper-citation-record.v1
2402.08523 v3

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 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T21:16:46.505775Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T20:33:03.209542Z

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 9721a59a-33ce-4d6d-b7c9-c31841076022 · inbound

Cutting is All You Need: Execution of Large-Scale Quantum Neural Networks on Limited-Qubit Devices cites this paper.

Cutting is All You Need: Execution of Large-Scale Quantum Neural Networks on Limited-Qubit Devices Investigating the Effect of Noise on the Training Performance of Hybrid Quantum Neural Networks

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-11T21:16:46.505775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T21:16:46.505775Z digest=sha256:ea03a2281944aab044cd097b1a4b11f72c4f2ca3c6c9e9c24d573f16c87e49b5

Observation 7a3ffd3f-8bd0-46b5-8858-bcd96c4ee87e · inbound

Modeling Feature Maps for Quantum Machine Learning cites this paper.

Modeling Feature Maps for Quantum Machine Learning Investigating the Effect of Noise on the Training Performance of Hybrid Quantum Neural Networks

Reference 14

Resolution
verified exact
local_arxiv, observed 2026-08-10T20:33:03.216715Z

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=2026-08-10T20:33:03.034012Z digest=sha256:63fa92cbb6e681e7c7361adfef4438f9f3e9ffa3cb00d7f9906c1edfdafc7c34