Paper Citation Record · LEDGER

Quantum-PEFT: Ultra parameter-efficient fine-tuning

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 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 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:34:13.877813Z

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 5adfa46e-d7cc-4f9c-8c71-6f722ba268b6 · inbound

$\mu$-MoE: Test-Time Pruning as Micro-Grained Mixture-of-Experts cites this paper.

$\mu$-MoE: Test-Time Pruning as Micro-Grained Mixture-of-Experts Quantum-PEFT: Ultra parameter-efficient fine-tuning

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T14:34:13.877813Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9e938e7-e4c8-4b82-9d3b-be251e0f39db · inbound

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models cites this paper.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Quantum-PEFT: Ultra parameter-efficient fine-tuning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T14:28:15.279162Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-08T06:32:00.761636+00:00.

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 2026-08-08T06:32:00.761636+00:00.