Paper Citation Record · LEDGER

A Reinforcement Learning approach for Quantum State Engineering

As of 16 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:1908.05981.

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

pith.paper-citation-record.v1
1908.05981 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:04:39.333721Z

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

17 of 17 outbound references displayed

verified exact2
verified fuzzy1
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4a402075-9196-4b16-af8c-4a70ba643d1c · outbound

This paper cites Scientific Reports 2:400, doi:10.1038/srep00400, ://www.nature.com/articles/srep00400.

A Reinforcement Learning approach for Quantum State Engineering Scientific Reports 2:400, doi:10.1038/srep00400, ://www.nature.com/articles/srep00400

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-14T13:04:39.255398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 819d356f-7666-4731-bf0c-fbec7d430756 · outbound

This paper cites Physical Review X 8(3):031086, doi:10.1103/PhysRevX.8.031086, ://link.aps.org/doi/10.1103/PhysRevX.8.031086.

A Reinforcement Learning approach for Quantum State Engineering Physical Review X 8(3):031086, doi:10.1103/PhysRevX.8.031086, ://link.aps.org/doi/10.1103/PhysRevX.8.031086

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-14T13:04:39.260868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc20b614-1e96-4386-9b49-ccf9b688c686 · outbound

This paper cites The nitrogen-vacancy colour centre in diamond.

A Reinforcement Learning approach for Quantum State Engineering The nitrogen-vacancy colour centre in diamond

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-14T13:04:39.265837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e698a0db-5c9b-46e3-81fc-0dde81bd2e3c · outbound

This paper cites Scientific Reports 7, doi:10.1038/s41598-017-00603-z, ://www.ncbi.nlm.nih.gov/pmc/articles/PMC5428879/.

A Reinforcement Learning approach for Quantum State Engineering Scientific Reports 7, doi:10.1038/s41598-017-00603-z, ://www.ncbi.nlm.nih.gov/pmc/articles/PMC5428879/

Reference 4

Resolution: verified exact; doi, observed 2026-08-14T13:04:39.415894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation aa83c428-5fe6-4c5a-9a76-6e7fb38eecf0 · outbound

This paper cites In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence , AAAI Press, Phoenix, Arizona, AAAI '16, pp 2094--2100, ://dl.acm.org/citation.cfm?id=3016100.3016191.

A Reinforcement Learning approach for Quantum State Engineering In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence , AAAI Press, Phoenix, Arizona, AAAI '16, pp 2094--2100, ://dl.acm.org/citation.cfm?id=3016100.3016191

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-14T13:04:39.276808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da67a89a-8638-4796-8b0b-5152df7b5660 · outbound

This paper cites Deep Recurrent Q-Learning for Partially Observable MDPs.

A Reinforcement Learning approach for Quantum State Engineering Deep Recurrent Q-Learning for Partially Observable MDPs

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-14T13:04:39.281619Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5847bda-f6d7-4dc1-844b-7a21bcbc1bdb · outbound

This paper cites Neural Information Processing Systems 25, doi:10.1145/3065386.

A Reinforcement Learning approach for Quantum State Engineering Neural Information Processing Systems 25, doi:10.1145/3065386

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-14T13:04:39.287552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc6656f7-f4ac-4ff4-afe9-4f11caa4d770 · outbound

This paper cites Phrase-Based & Neural Unsupervised Machine Translation.

A Reinforcement Learning approach for Quantum State Engineering Phrase-Based & Neural Unsupervised Machine Translation

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-14T13:04:39.292962Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6094c693-c062-456a-a924-6a75a54c4b04 · outbound

This paper cites A high-bias, low-variance introduction to Machine Learning for physicists.

A Reinforcement Learning approach for Quantum State Engineering A high-bias, low-variance introduction to Machine Learning for physicists

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-14T13:04:39.298440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a112795-3360-4ad6-8c3c-3d658c167efc · outbound

This paper cites Proceedings of the National Academy of Sciences 115(6):1221--1226, doi:10.1073/pnas.1714936115, ://www.pnas.org/content/115/6/1221.

A Reinforcement Learning approach for Quantum State Engineering Proceedings of the National Academy of Sciences 115(6):1221--1226, doi:10.1073/pnas.1714936115, ://www.pnas.org/content/115/6/1221

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-14T13:04:39.303403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3b0806f-c1b2-4f6d-b409-750c9a4b233e · outbound

This paper cites Nature 518(7540):529--533, ://www.nature.com/articles/nature14236.

A Reinforcement Learning approach for Quantum State Engineering Nature 518(7540):529--533, ://www.nature.com/articles/nature14236

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:04:39.641331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a4a085f6-e799-44dc-834c-75976a6783b7 · outbound

This paper cites Large-Scale Evolution of Image Classifiers.

A Reinforcement Learning approach for Quantum State Engineering Large-Scale Evolution of Image Classifiers

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-14T13:04:39.311444Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a0a031b-70ef-4837-9c2b-66cf86ddc72e · outbound

This paper cites Science 362(6419):1140--1144, doi:10.1126/science.aar6404, ://science.sciencemag.org/content/362/6419/1140.

A Reinforcement Learning approach for Quantum State Engineering Science 362(6419):1140--1144, doi:10.1126/science.aar6404, ://science.sciencemag.org/content/362/6419/1140

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-14T13:04:39.315573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6e6289d-04a4-453b-a25a-2cf9f3c4198e · outbound

This paper cites an unresolved cited work.

A Reinforcement Learning approach for Quantum State Engineering Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-14T13:04:39.625343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 137dc139-4a98-44c5-81fb-309cc94d1b20 · outbound

This paper cites Journal of Magnetic Resonance 269:225--236, doi:10.1016/j.jmr.2016.06.017.

A Reinforcement Learning approach for Quantum State Engineering Journal of Magnetic Resonance 269:225--236, doi:10.1016/j.jmr.2016.06.017

Reference 15

Resolution: verified exact; doi, observed 2026-08-14T13:04:39.370999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a368824f-477d-4ba6-a4d5-437f424f41e4 · outbound

This paper cites , " * write output.state after.block = add.period write newline.

A Reinforcement Learning approach for Quantum State Engineering , " * write output.state after.block = add.period write newline

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-14T13:04:39.328663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae6ad375-689b-4a54-97ca-29b730fc6b13 · outbound

This paper cites write newline.

A Reinforcement Learning approach for Quantum State Engineering write newline

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-14T13:04:39.333721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.