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Paper Citation Record · LEDGER

Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1910.06151.

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

pith.paper-citation-record.v1
1910.06151 v4

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 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:15:03.333969Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T14:15:05.628552Z

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 2314123c-f596-4cce-aed1-754628a00387 · inbound

A distillation-teleportation protocol for fault-tolerant QRAM cites this paper.

A distillation-teleportation protocol for fault-tolerant QRAM Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning

Reference 113

Resolution
verified exact
local_arxiv, observed 2026-08-07T14:15:05.712463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:15:03.333969Z digest=sha256:5f53d0c00239185029092d6ed63e1fca89d0fabe1b5dda12915f3228b3ba28bf

Observation 9440c4fe-5703-4ba2-b807-10dabab49c44 · inbound

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency cites this paper.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning

Reference 14

Resolution
unresolved
no resolver link, observed 2026-07-31T23:26:41.606788Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T23:26:41.606788Z digest=sha256:e5fbe3bb8620d4379a9d050e23c5bba35cd50594a4f9e27261da836bff8629fc