Paper Citation Record · LEDGER

Quantum Generative Training Using R\'enyi Divergences

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2106.09567.

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

pith.paper-citation-record.v1
2106.09567 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T12:11:56.947731Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T22:05:50.028078Z

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 5e05e990-6015-4938-a3d2-b545f53104d6 · inbound

Quantum Convolutional Neural Networks are Effectively Classically Simulable cites this paper.

Quantum Convolutional Neural Networks are Effectively Classically Simulable Quantum Generative Training Using R\'enyi Divergences

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-23T22:05:50.030896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a4a108c0-a64f-4ecc-b348-37d4acf40f90 · inbound

Pitfalls when tackling the exponential concentration of parameterized quantum models cites this paper.

Pitfalls when tackling the exponential concentration of parameterized quantum models Quantum Generative Training Using R\'enyi Divergences

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-06T12:11:56.947731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e649df6f-7dfe-4b24-9c75-008411341b8b · inbound

A hardware efficient quantum residual neural network without post-selection cites this paper.

A hardware efficient quantum residual neural network without post-selection Quantum Generative Training Using R\'enyi Divergences

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T05:36:02.906907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 12692dd1-03b5-424e-a69c-23875ceb9424 · inbound

A hardware efficient quantum residual neural network without post-selection cites this paper.

A hardware efficient quantum residual neural network without post-selection Quantum Generative Training Using R\'enyi Divergences

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-05-22T10:54:47.592458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1b45e2dc-a549-40a3-b41e-76057354b2f1 · inbound

Trainability Beyond Linearity in Variational Quantum Objectives cites this paper.

Trainability Beyond Linearity in Variational Quantum Objectives Quantum Generative Training Using R\'enyi Divergences

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T12:11:04.645623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ec994b6d-58b4-4d03-a271-d7902ba93f12 · inbound

Quantum Tilted Loss in Variational Optimization: Theory and Applications cites this paper.

Quantum Tilted Loss in Variational Optimization: Theory and Applications Quantum Generative Training Using R\'enyi Divergences

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:36:06.867352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.