Paper Citation Record · LEDGER

A Survey on Quantum Reinforcement Learning

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 19 inbound Pith citation observations for arXiv:2211.03464.

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

pith.paper-citation-record.v1
2211.03464 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 19 of 19 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T21:05:12.327417Z

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

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verified fuzzy0
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External citation measurements

32
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 cce63520-b113-41e3-91a7-26c311675de0 · inbound

Quantum framework for Reinforcement Learning: Integrating Markov decision process, quantum arithmetic, and trajectory search cites this paper.

Quantum framework for Reinforcement Learning: Integrating Markov decision process, quantum arithmetic, and trajectory search A Survey on Quantum Reinforcement Learning

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-23T06:37:38.895859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b06e5e74-95d6-4343-bc2a-8fd02d0b64ba · inbound

Benchmarking Quantum Reinforcement Learning cites this paper.

Benchmarking Quantum Reinforcement Learning A Survey on Quantum Reinforcement Learning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-08T21:05:12.327417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e4ddae8-b96f-4b2b-a297-b4ab170fa219 · inbound

Quantum AIXI: Universal Intelligence via Quantum Information cites this paper.

Quantum AIXI: Universal Intelligence via Quantum Information A Survey on Quantum Reinforcement Learning

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T13:44:34.644814Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41a2e165-947e-4d8a-8842-78cffd58de8c · inbound

Quantum computing and artificial intelligence: status and perspectives cites this paper.

Quantum computing and artificial intelligence: status and perspectives A Survey on Quantum Reinforcement Learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T12:53:58.404550Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9bb2ff74-cee8-4c4a-a794-3d3ca8e8c67d · inbound

Learning Encodings by Maximizing State Distinguishability: Variational Quantum Error Correction cites this paper.

Learning Encodings by Maximizing State Distinguishability: Variational Quantum Error Correction A Survey on Quantum Reinforcement Learning

Reference 70

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T09:57:14.086654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1bc370a5-53fc-401b-8024-4ad470453a1e · inbound

Quantum Algorithms for Bandits with Knapsacks with Improved Regret and Time Complexities cites this paper.

Quantum Algorithms for Bandits with Knapsacks with Improved Regret and Time Complexities A Survey on Quantum Reinforcement Learning

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-06T19:57:39.379508Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 120f4ad5-7c33-445e-98f2-0d43422277f7 · inbound

Quantum Reinforcement Learning by Adaptive Non-local Observables cites this paper.

Quantum Reinforcement Learning by Adaptive Non-local Observables A Survey on Quantum Reinforcement Learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T14:16:40.412694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7129a32-2930-468e-b407-5cccfb7f8794 · inbound

Scalable Quantum Reinforcement Learning on NISQ Devices with Dynamic-Circuit Qubit Reuse and Grover Optimization cites this paper.

Scalable Quantum Reinforcement Learning on NISQ Devices with Dynamic-Circuit Qubit Reuse and Grover Optimization A Survey on Quantum Reinforcement Learning

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-18T15:31:33.609474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8698961b-3eea-42c7-a327-04b5031e2fb2 · inbound

Complexity scaling and optimal policy degeneracy in quantum reinforcement learning via analytically solvable unitary-control-then-measure models cites this paper.

Complexity scaling and optimal policy degeneracy in quantum reinforcement learning via analytically solvable unitary-control-then-measure models A Survey on Quantum Reinforcement Learning

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:30:59.907591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d84c2b34-7957-49ab-89ed-0ed0fc1feb66 · inbound

Complexity scaling and optimal policy degeneracy in quantum reinforcement learning via analytically solvable unitary-control-then-measure models cites this paper.

Complexity scaling and optimal policy degeneracy in quantum reinforcement learning via analytically solvable unitary-control-then-measure models A Survey on Quantum Reinforcement Learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-12T23:50:25.262273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b184cfa-7a10-4522-979d-d665b0545add · inbound

Learning quantum disentanglement scheduling from reduced states via modular hybrid policies cites this paper.

Learning quantum disentanglement scheduling from reduced states via modular hybrid policies A Survey on Quantum Reinforcement Learning

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:21:28.867148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 468fd957-0e85-4170-aef2-72ac8d336973 · inbound

Quantum Hierarchical Reinforcement Learning via Variational Quantum Circuits cites this paper.

Quantum Hierarchical Reinforcement Learning via Variational Quantum Circuits A Survey on Quantum Reinforcement Learning

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T11:01:30.373743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5e4c8a8f-5f4e-47ef-9c20-39d5271a5961 · inbound

Quantum Adversarial Machine Learning: From Classical Adaptations to Quantum-Native Methods cites this paper.

Quantum Adversarial Machine Learning: From Classical Adaptations to Quantum-Native Methods A Survey on Quantum Reinforcement Learning

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-20T22:29:09.484695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fd4975ae-c3e9-4e64-9f72-dfb227e7568f · inbound

Q-SpiRL: Quantum Spiking Reinforcement Learning for Adaptive Robot Navigation cites this paper.

Q-SpiRL: Quantum Spiking Reinforcement Learning for Adaptive Robot Navigation A Survey on Quantum Reinforcement Learning

Reference 26

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T04:53:58.091271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 47e8f15d-669a-432c-8062-8ad8ab9b0ebb · inbound

Q-SpiRL: Quantum Spiking Reinforcement Learning for Adaptive Robot Navigation cites this paper.

Q-SpiRL: Quantum Spiking Reinforcement Learning for Adaptive Robot Navigation A Survey on Quantum Reinforcement Learning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-02T13:36:11.689765Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb7c316a-c22c-4ef8-bcd6-188d5281603e · inbound

Enhanced Reinforcement Learning-based Process Synthesis via Quantum Computing cites this paper.

Enhanced Reinforcement Learning-based Process Synthesis via Quantum Computing A Survey on Quantum Reinforcement Learning

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-21T04:33:57.746607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 44e6d95c-3580-494c-9cab-14be5594aabe · inbound

Research progress on quantum neural networks and quantum machine learning cites this paper.

Research progress on quantum neural networks and quantum machine learning A Survey on Quantum Reinforcement Learning

Reference 159

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T19:26:00.878817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 73c1f794-583e-4b3e-810f-88dfed9abc76 · inbound

Recursive QLSTM with Dynamic Variational Quantum Circuit Adaptation cites this paper.

Recursive QLSTM with Dynamic Variational Quantum Circuit Adaptation A Survey on Quantum Reinforcement Learning

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-06-26T08:49:15.590965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d53c94ad-4a96-4039-9aa6-d7544255bf36 · inbound

Quantum-enhanced Monte Carlo Tree Search framework for combinatorial optimization problems cites this paper.

Quantum-enhanced Monte Carlo Tree Search framework for combinatorial optimization problems A Survey on Quantum Reinforcement Learning

Reference 79

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T06:24:19.201247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.