Paper Citation Record · LEDGER

Application of Quantum Annealing to Training of Deep Neural Networks

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:1510.06356.

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

pith.paper-citation-record.v1
1510.06356 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 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 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:19:28.675321Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-03T00:07:27.859766Z

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 55d1ecfa-49e5-47d4-a39c-5d70ccac76c8 · inbound

The Capacity of Quantum Neural Networks cites this paper.

The Capacity of Quantum Neural Networks Application of Quantum Annealing to Training of Deep Neural Networks

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-14T15:21:06.441156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 657aab9e-753d-4dfe-a7ed-0b5871d10322 · inbound

Boltzmann Sampling by Diabatic Quantum Annealing cites this paper.

Boltzmann Sampling by Diabatic Quantum Annealing Application of Quantum Annealing to Training of Deep Neural Networks

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-05-23T20:55:49.032199Z

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.

Observation ca1b0a93-f4bd-4a6a-8802-c0a747a5ab7a · inbound

Implementing Large Quantum Boltzmann Machines as Generative AI Models for Dataset Balancing cites this paper.

Implementing Large Quantum Boltzmann Machines as Generative AI Models for Dataset Balancing Application of Quantum Annealing to Training of Deep Neural Networks

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-09T06:02:55.144879Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74e1c932-dd05-42ce-85e5-7df9c7f4fad3 · inbound

Optimized Quantum Embedding: A Universal Minor-Embedding Framework for Large Complete Bipartite Graph cites this paper.

Optimized Quantum Embedding: A Universal Minor-Embedding Framework for Large Complete Bipartite Graph Application of Quantum Annealing to Training of Deep Neural Networks

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T05:19:28.675321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2df14f4e-fd6e-4fde-a863-ba6ad2031d57 · inbound

Bridging Brains and Machines: A Unified Frontier in Neuroscience, Artificial Intelligence, and Neuromorphic Systems cites this paper.

Bridging Brains and Machines: A Unified Frontier in Neuroscience, Artificial Intelligence, and Neuromorphic Systems Application of Quantum Annealing to Training of Deep Neural Networks

Reference 249

Resolution: verified exact; local_arxiv, observed 2026-05-19T04:42:04.916615Z

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.

Observation 44f3269b-8e10-448f-a3b6-b48882ae9286 · inbound

Quantum Boltzmann Machines using Parallel Annealing for Medical Image Classification cites this paper.

Quantum Boltzmann Machines using Parallel Annealing for Medical Image Classification Application of Quantum Annealing to Training of Deep Neural Networks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T16:16:20.336337Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5288642a-5c58-48ef-b1ff-e245fd11f1f4 · inbound

Quantum Annealing: Optimisation, Sampling, and Many-Body Dynamics cites this paper.

Quantum Annealing: Optimisation, Sampling, and Many-Body Dynamics Application of Quantum Annealing to Training of Deep Neural Networks

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:10:59.380841Z

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.

Observation 5e30cea5-8271-4092-988f-a807265e9dbc · inbound

A Quantum Inspired Variational Kernel and Explainable AI Framework for Cross Region Solar and Wind Energy Forecasting cites this paper.

A Quantum Inspired Variational Kernel and Explainable AI Framework for Cross Region Solar and Wind Energy Forecasting Application of Quantum Annealing to Training of Deep Neural Networks

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-12T01:56:15.122417Z

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.

Observation c27bc54d-ca03-41cf-b3ef-075eb991bb31 · inbound

Optimizing Energy-based Neural Network Training with Coherent Ising Machine cites this paper.

Optimizing Energy-based Neural Network Training with Coherent Ising Machine Application of Quantum Annealing to Training of Deep Neural Networks

Reference 10

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T00:07:27.861160Z

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.

Observation 32b1873e-bf0d-4963-a640-36685a212dcb · inbound

Mixed-Binary Quadratic Programming via QUBO Sampling without Continuous-Variable Binarization cites this paper.

Mixed-Binary Quadratic Programming via QUBO Sampling without Continuous-Variable Binarization Application of Quantum Annealing to Training of Deep Neural Networks

Reference 145

Resolution: unresolved; no resolver link, observed 2026-08-01T07:58:17.776796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.