Pith. sign in

Paper Citation Record · LEDGER

Power of data in quantum machine learning

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2011.01938.

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

pith.paper-citation-record.v1
2011.01938 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T22:20:45.556484Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T11:53:24.384731Z

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 f202b470-de66-4d5f-b649-d5bd6c16c463 · inbound

Efficient classical training of model-free quantum photonic reservoir cites this paper.

Efficient classical training of model-free quantum photonic reservoir Power of data in quantum machine learning

Reference 73

Resolution
verified exact
arxiv_id, observed 2026-05-11T11:21:03.153488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T15:00:12.133135Z digest=sha256:9800fb45554e472b08119c681f0e6f1a128c880956ffd29347db439b8371b60a

Observation b294e878-1e50-4569-86a8-d5201845085d · inbound

Quantum encodings that preserve persistent homology cites this paper.

Quantum encodings that preserve persistent homology Power of data in quantum machine learning

Reference 109

Resolution
verified exact
arxiv_id, observed 2026-06-29T11:53:24.386166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-29T11:45:53.392594Z digest=sha256:994175fbd1d21708f3adce00dd55b55dcfc965fdd6dfa6bcf0e39dad2059efa3

Observation c2aec0ce-6506-43e2-9a90-770c35209fb6 · 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 Power of data in quantum machine learning

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-01T22:20:45.556484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T22:20:45.556484Z digest=sha256:e54ddbeaaf9bbdf1024bffa3455c7c25b4dfee88d8c8bd383fc24cd11bcd2647