Paper Citation Record · LEDGER

A quantum algorithm to train neural networks using low-depth circuits

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:1712.05304.

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

pith.paper-citation-record.v1
1712.05304 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T15:21:06.437111Z

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
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

85
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 57f086e3-99ec-4be0-abae-d9f85dc44c7e · inbound

PennyLane: Automatic differentiation of hybrid quantum-classical computations cites this paper.

PennyLane: Automatic differentiation of hybrid quantum-classical computations A quantum algorithm to train neural networks using low-depth circuits

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T15:14:44.127663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3715dc0e-f600-4f8a-b69c-565a872ef341 · inbound

Learning to learn with quantum neural networks via classical neural networks cites this paper.

Learning to learn with quantum neural networks via classical neural networks A quantum algorithm to train neural networks using low-depth circuits

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-24T23:00:03.232589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 66ec8b43-b172-496f-b46f-9aceedd3b341 · inbound

The Capacity of Quantum Neural Networks cites this paper.

The Capacity of Quantum Neural Networks A quantum algorithm to train neural networks using low-depth circuits

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-14T15:21:06.437111Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d4ea4c8-6eb2-4480-a4ef-c03122638ff2 · inbound

A quantum algorithm to count weighted ground states of classical spin Hamiltonians cites this paper.

A quantum algorithm to count weighted ground states of classical spin Hamiltonians A quantum algorithm to train neural networks using low-depth circuits

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-14T15:12:27.655748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08474f0f-b5cb-46de-885c-c8cd753c53c9 · inbound

Optimizing quantum heuristics with meta-learning cites this paper.

Optimizing quantum heuristics with meta-learning A quantum algorithm to train neural networks using low-depth circuits

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-14T14:26:55.763903Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5f22bac-a10b-4c9f-b7ac-5a4921ff2c43 · inbound

Training the Quantum Approximate Optimization Algorithm without access to a Quantum Processing Unit cites this paper.

Training the Quantum Approximate Optimization Algorithm without access to a Quantum Processing Unit A quantum algorithm to train neural networks using low-depth circuits

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-14T11:33:55.126576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85756700-f88a-45c0-a45f-9607a111b4d7 · inbound

Variational quantum thermalizers based on weakly-symmetric nonunitary multi-qubit operations cites this paper.

Variational quantum thermalizers based on weakly-symmetric nonunitary multi-qubit operations A quantum algorithm to train neural networks using low-depth circuits

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T20:55:26.431592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5149eb3-f601-4448-b1b4-1d5b203d3d64 · inbound

Scaling Quantum Algorithms via Dissipation: Avoiding Barren Plateaus cites this paper.

Scaling Quantum Algorithms via Dissipation: Avoiding Barren Plateaus A quantum algorithm to train neural networks using low-depth circuits

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T20:54:57.828844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55f1989c-5042-4535-a2b7-2384d1c8219e · inbound

A review of quantum machine learning and quantum-inspired applied methods to computational fluid dynamics cites this paper.

A review of quantum machine learning and quantum-inspired applied methods to computational fluid dynamics A quantum algorithm to train neural networks using low-depth circuits

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-05-18T06:42:26.349411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.