Paper Citation Record · LEDGER

Trainability and Expressivity of Hamming-Weight Preserving Quantum Circuits for Machine Learning

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2309.15547.

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

pith.paper-citation-record.v1
2309.15547 v4

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measured 0 of 0 reference resolution

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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 2026-08-06T12:11:56.869098Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T15:07:19.388461Z

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation a105d20b-484b-4d42-8644-d6685b2b5528 · inbound

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

Quantum Convolutional Neural Networks are Effectively Classically Simulable Trainability and Expressivity of Hamming-Weight Preserving Quantum Circuits for Machine Learning

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-23T22:05:49.960004Z

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 3399f651-6a5d-4bc1-a88d-5d54f10d508f · inbound

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

Pitfalls when tackling the exponential concentration of parameterized quantum models Trainability and Expressivity of Hamming-Weight Preserving Quantum Circuits for Machine Learning

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-06T12:11:56.869098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86fbc763-a534-4edb-a0cb-848d2f241b93 · inbound

Mitigating the barren plateau problem in linear optics cites this paper.

Mitigating the barren plateau problem in linear optics Trainability and Expressivity of Hamming-Weight Preserving Quantum Circuits for Machine Learning

Reference 86

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T10:26:14.532292Z

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 8805d448-4ffe-479c-9d4c-928772b0d583 · inbound

Symmetries and overparametrization properties of Hamiltonian variational ansatzes for the $(1+1)$d $\mathbb{Z}_2$ lattice gauge theory cites this paper.

Symmetries and overparametrization properties of Hamiltonian variational ansatzes for the $(1+1)$d $\mathbb{Z}_2$ lattice gauge theory Trainability and Expressivity of Hamming-Weight Preserving Quantum Circuits for Machine Learning

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-07-02T13:06:59.294464Z

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 6319638d-ad23-4f6b-8b70-de4c4ba6a623 · inbound

The Dynamical Lie Algebra of QAOA-MaxCut on the Complete Graph cites this paper.

The Dynamical Lie Algebra of QAOA-MaxCut on the Complete Graph Trainability and Expressivity of Hamming-Weight Preserving Quantum Circuits for Machine Learning

Reference 53

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T12:16:56.558705Z

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 8e4406c0-ddfe-4812-a463-aa236cc60930 · inbound

A hardware-efficient variational ansatz with an exact diagonal metric for real- and imaginary-time evolution and Haar sampling cites this paper.

A hardware-efficient variational ansatz with an exact diagonal metric for real- and imaginary-time evolution and Haar sampling Trainability and Expressivity of Hamming-Weight Preserving Quantum Circuits for Machine Learning

Reference 72

Resolution: verified exact; local_arxiv, observed 2026-07-10T15:07:19.389880Z

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.