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Paper Citation Record · LEDGER

Efficient simulation of parametrized quantum circuits under non-unital noise through Pauli backpropagation

As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2501.13050.

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

pith.paper-citation-record.v1
2501.13050 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 2026-08-06T06:34:29.942622+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-21T09:47:06.645443Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T09:49:57.417052Z

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 a0d98aef-b6fb-4be1-9108-a28168718b7d · inbound

Quantitative Universal Approximation for Noisy Quantum Neural Networks cites this paper.

Quantitative Universal Approximation for Noisy Quantum Neural Networks Efficient simulation of parametrized quantum circuits under non-unital noise through Pauli backpropagation

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-05-13T21:33:18.404142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-13T21:30:55.729343Z digest=sha256:78e42525ddf423839e9df01b61d4974481d31be5a55d1b07129dbac87a34be75

Observation 1af7ad6b-6769-4485-adbf-d352397718f8 · inbound

Quantitative Universal Approximation for Noisy Quantum Neural Networks cites this paper.

Quantitative Universal Approximation for Noisy Quantum Neural Networks Efficient simulation of parametrized quantum circuits under non-unital noise through Pauli backpropagation

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-05-21T09:49:57.418913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-21T09:47:06.645443Z digest=sha256:95ad7071f8f887b4f41fa8f60da56e7a81011eb29b5d67b97250e4389abdf3ed