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Paper Citation Record · LEDGER

Variational Quantum Circuits for Deep Reinforcement Learning

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1907.00397.

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

pith.paper-citation-record.v1
1907.00397 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T05:31:47.956954Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T06:22:07.593040Z

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 46bc51f1-e172-4a36-bcf5-43cd47aa0078 · inbound

Quantum Machine Learning for State Tomography Using Classical Data cites this paper.

Quantum Machine Learning for State Tomography Using Classical Data Variational Quantum Circuits for Deep Reinforcement Learning

Reference 83

Resolution
verified exact
arxiv_id, observed 2026-05-19T06:22:07.595610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-19T06:20:49.682647Z digest=sha256:49c21c3c0607f993183d2e748e69fdca527fd0e2ca76b6a3cc520f2ea99ff46a

Observation ae3d7a00-d1a2-428c-b124-0b6fff9711f8 · inbound

First Experience with Real-Time Control Using Simulated VQC-Based Quantum Policies cites this paper.

First Experience with Real-Time Control Using Simulated VQC-Based Quantum Policies Variational Quantum Circuits for Deep Reinforcement Learning

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-06T05:31:47.956954Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T05:31:47.956954Z digest=sha256:ac885a441c94fb6fc89569f83433f2503e6b7c8c9828b18cbd71684b355129db

Observation 2c0837cd-5829-451a-8889-da096c75ff1c · 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 Variational Quantum Circuits for Deep Reinforcement Learning

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-05T14:33:21.360463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:33:21.360463Z digest=sha256:115f7b594465588f50dbbe09e3a406d7628319580cf8ae5f81229ff9acd9f82d