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Paper Citation Record · LEDGER

Quantum Deep Reinforcement Learning for Robot Navigation Tasks

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2202.12180.

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

pith.paper-citation-record.v1
2202.12180 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:49:16.255707Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T14:33:26.407167Z

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 0812ba29-95d3-48c9-bf12-dba4a908b627 · inbound

Quantum Artificial Intelligence for Secure Autonomous Vehicle Navigation: An Architectural Proposal cites this paper.

Quantum Artificial Intelligence for Secure Autonomous Vehicle Navigation: An Architectural Proposal Quantum Deep Reinforcement Learning for Robot Navigation Tasks

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-06T23:49:16.255707Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:49:16.255707Z digest=sha256:bec2595d246cf09ec023eaab9ce085c4b24f7d896a1b5b3a1abb0432e969d46f

Observation 65ae3828-081b-4353-b370-6ef546cf793b · inbound

HCQA: Hybrid Classical-Quantum Agent for Generating Optimal Quantum Sensor Circuits cites this paper.

HCQA: Hybrid Classical-Quantum Agent for Generating Optimal Quantum Sensor Circuits Quantum Deep Reinforcement Learning for Robot Navigation Tasks

Reference 1

Resolution
verified exact
local_arxiv, observed 2026-08-05T14:33:26.463216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-05T14:33:21.020372Z digest=sha256:bc99e724b86bccb459c304074aed45eb66f3ef828468288a022b6a0144068267