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Paper Citation Record · LEDGER

Quantum Generative Training Using R\'enyi Divergences

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 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 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:50:51.132847Z

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-23T22:05:20.412426Z digest=sha256:65d8bb9f3297880101c2088f4e646d46d9836482bead7cd3c2163d7f079fc13d

Observation 67f33660-0b8e-4276-9e0e-674a7f286ad9 · inbound

iHQGAN: A Lightweight Invertible Hybrid Quantum-Classical Generative Adversarial Network for Unsupervised Image-to-Image Translation cites this paper.

iHQGAN: A Lightweight Invertible Hybrid Quantum-Classical Generative Adversarial Network for Unsupervised Image-to-Image Translation Quantum Generative Training Using R\'enyi Divergences

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-12T15:50:51.132847Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:50:51.132847Z digest=sha256:83ee77e9a993a09e7a894762b85ffadd2905d2e89b2ffe1ced28cc40fb3423b3

Observation 463427a6-7542-4b9d-806e-d7e3e8a7932b · inbound

A unifying account of warm start guarantees for patches of quantum landscapes cites this paper.

A unifying account of warm start guarantees for patches of quantum landscapes Quantum Generative Training Using R\'enyi Divergences

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-08T11:35:58.612837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T11:35:58.612837Z digest=sha256:ba11d4a8962f2696dad38d76b6d9576332fe1135a788954ca8fd4a89b4b5c93f

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.

source=pdf_text observed=2026-08-06T12:11:56.947731Z digest=sha256:55cc9997037f83c6ea26ebd6d623245e300571051b3c33eb2763c06f83f33a17

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-10T18:02:04.838369Z digest=sha256:bdc161b83779388559fc73621084f6ea5171d8e234e8887898b8b27d63d70860

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-22T10:54:21.066630Z digest=sha256:03ae12324513c7914dbd660fcec7e9057fd57cb890a31e487fdbec512d037638

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-10T04:07:06.038782Z digest=sha256:23c846f0e465f6ec09e3af89839e1b104daf857fd7702ad48f5710f6b362c73c

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-09T16:09:45.904791Z digest=sha256:1c964f53b7ba6207d360aaf6bab7b1173550ff94c3d4c98c4bfe045bae03aa21