Paper Citation Record · LEDGER

On the relation between trainability and dequantization of variational quantum learning models

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2406.07072.

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

pith.paper-citation-record.v1
2406.07072 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T21:00:12.154386Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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malformed identifier0
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External citation measurements

1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation ebdaca09-d774-482a-897a-c1fcacbc73e8 · inbound

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

Quantum Convolutional Neural Networks are Effectively Classically Simulable On the relation between trainability and dequantization of variational quantum learning models

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-23T22:05:49.936534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7a85d1b5-a7ee-4d73-90c4-ab3cc63a33fc · inbound

Architectural Patterns for Designing Quantum Artificial Intelligence Systems cites this paper.

Architectural Patterns for Designing Quantum Artificial Intelligence Systems On the relation between trainability and dequantization of variational quantum learning models

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-12T21:00:12.154386Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2868f61f-cf6a-4992-90c8-537e375db002 · inbound

The role of data-induced randomness in quantum machine learning classification tasks cites this paper.

The role of data-induced randomness in quantum machine learning classification tasks On the relation between trainability and dequantization of variational quantum learning models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T10:35:11.896957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1299ed2b-08da-4754-bf7f-41c2dc54b23b · inbound

Opportunities and limitations of explaining quantum machine learning cites this paper.

Opportunities and limitations of explaining quantum machine learning On the relation between trainability and dequantization of variational quantum learning models

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-11T12:02:09.216745Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86581ecd-bcda-4d32-be18-8f55f9e54721 · inbound

Variational decision diagrams for quantum-inspired machine learning applications cites this paper.

Variational decision diagrams for quantum-inspired machine learning applications On the relation between trainability and dequantization of variational quantum learning models

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-23T03:52:29.595364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8c791d68-caa9-4535-a40e-b5cba5b12d6a · inbound

Quantum Neural Networks for Cloud Cover Parameterizations in Climate Models cites this paper.

Quantum Neural Networks for Cloud Cover Parameterizations in Climate Models On the relation between trainability and dequantization of variational quantum learning models

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T19:24:32.339734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9adb2e95-fd28-4414-982a-3e3ac0a9e1ac · inbound

Iterative Quantum Feature Maps cites this paper.

Iterative Quantum Feature Maps On the relation between trainability and dequantization of variational quantum learning models

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-19T08:17:10.629134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2edad1c7-b56f-42f5-8573-ca0a1eccbb5f · inbound

Quantum reinforcement learning in dynamic environments cites this paper.

Quantum reinforcement learning in dynamic environments On the relation between trainability and dequantization of variational quantum learning models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T20:53:50.410029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5742f7d-a969-4c8c-814a-1c5bd29ae537 · inbound

Mind the gaps: The fraught road to quantum advantage cites this paper.

Mind the gaps: The fraught road to quantum advantage On the relation between trainability and dequantization of variational quantum learning models

Reference 125

Resolution: verified exact; arxiv_id, observed 2026-05-18T01:51:39.733898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 09ebe910-43c8-4007-9ece-7ec030e8a57e · inbound

Mind the gaps: The fraught road to quantum advantage cites this paper.

Mind the gaps: The fraught road to quantum advantage On the relation between trainability and dequantization of variational quantum learning models

Reference 125

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:11:34.736481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2522995e-b3a7-409e-a556-875db004e34b · inbound

Exponential quantum advantage in processing massive classical data cites this paper.

Exponential quantum advantage in processing massive classical data On the relation between trainability and dequantization of variational quantum learning models

Reference 19

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T07:26:00.097291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 62be24e6-b946-4f00-ad9a-f1bf41b53dea · inbound

Benchmarking a machine-learning differential equations solver on a neutral-atom logical processor cites this paper.

Benchmarking a machine-learning differential equations solver on a neutral-atom logical processor On the relation between trainability and dequantization of variational quantum learning models

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-21T04:39:35.027715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.