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Paper Citation Record · LEDGER

Quantum-PEFT: Ultra parameter-efficient fine-tuning

As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2503.05431.

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

pith.paper-citation-record.v1
2503.05431 v1

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 2026-08-03T06:30:56.289259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T22:25:46.780183Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T23:07:26.491776Z

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 247a2c00-4cdc-46aa-b783-f87cd274805a · inbound

Research progress on quantum neural networks and quantum machine learning cites this paper.

Research progress on quantum neural networks and quantum machine learning Quantum-PEFT: Ultra parameter-efficient fine-tuning

Reference 225

Resolution
verified exact
arxiv_id, observed 2026-07-01T19:26:00.901666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-06-28T22:25:46.780183Z digest=sha256:b92cb3214876237cb4ad71e6ec56b1e8814c0ee147367bbd4320c4f1c4259636

Observation ed77df24-774b-4be4-b540-5b9579921fcc · inbound

EinSort: Sorting is All We Need for Tensorizing LLM cites this paper.

EinSort: Sorting is All We Need for Tensorizing LLM Quantum-PEFT: Ultra parameter-efficient fine-tuning

Reference 43

Resolution
verified exact
arxiv_id, observed 2026-07-02T23:07:26.493147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=arxiv_source observed=2026-06-27T18:31:01.804061Z digest=sha256:e7e6985473560d6e37f8643060ad238a6fe67be4827e7beea095b541cbd4056b