Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Potential and limitations of random Fourier features for dequantizing quantum machine learning
As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2309.11647.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T12:02:09.020874Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-23T05:37:36.889174Z
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 20e76aa7-5260-4cc1-9c89-60bc3b437ec3 · inbound
Opportunities and limitations of explaining quantum machine learning cites this paper.
Opportunities and limitations of explaining quantum machine learning Potential and limitations of random Fourier features for dequantizing quantum machine learning
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T12:02:09.020874Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation eb12f06a-f2ae-4462-8f8e-7f80d1eb49ef · inbound
IQPopt: Fast optimization of instantaneous quantum polynomial circuits in JAX cites this paper.
IQPopt: Fast optimization of instantaneous quantum polynomial circuits in JAX Potential and limitations of random Fourier features for dequantizing quantum machine learning
Reference 34
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T05:37:36.892414Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 37f36a40-3ac3-4174-8049-73364023fa2b · inbound
Quantum Neural Networks for Cloud Cover Parameterizations in Climate Models cites this paper.
Quantum Neural Networks for Cloud Cover Parameterizations in Climate Models Potential and limitations of random Fourier features for dequantizing quantum machine learning
Reference 109
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T19:24:33.005166Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b7729aa6-1a68-4502-9fb0-7604dde0be87 · inbound
Out of Tune: Demystifying Noise-Effects on Quantum Fourier Models cites this paper.
Out of Tune: Demystifying Noise-Effects on Quantum Fourier Models Potential and limitations of random Fourier features for dequantizing quantum machine learning
Reference 44
- Resolution
- malformed identifier
- no resolver link, observed 2026-08-07T04:53:38.907962Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d5a591f6-11e1-416c-b026-83320e3d7973 · inbound
The Fourier Wall: Why Public Tabular Datasets Refuse Quantum Advantage, and a Certified Recipe for Where It Lives cites this paper.
The Fourier Wall: Why Public Tabular Datasets Refuse Quantum Advantage, and a Certified Recipe for Where It Lives Potential and limitations of random Fourier features for dequantizing quantum machine learning
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T22:20:46.653485Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.