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Paper Citation Record · LEDGER

QFOR: A Fidelity-aware Orchestrator for Quantum Computing Environments using Deep Reinforcement Learning

As of 7 August 2026, this Paper Citation Record lists 3 of 3 outbound references and 3 inbound Pith citation observations for arXiv:2508.04974.

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

pith.paper-citation-record.v1
2508.04974 v1

Coverage vector

measured 3 of 3 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T23:40:56.420256Z

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T11:45:33.499117Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T14:28:31.443287Z

Reference resolution

3 of 3 outbound references displayed

  • verified exact0
  • verified fuzzy2
  • unresolved1
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7fcf74fa-3f19-413c-a111-6b398cbfafaf · outbound

This paper cites However, Gaussians are treated equally weighted for rendering in most 3DGS methods, making them prone to overfitting, which is particularly the case in sparse-view sce- narios.

QFOR: A Fidelity-aware Orchestrator for Quantum Computing Environments using Deep Reinforcement Learning However, Gaussians are treated equally weighted for rendering in most 3DGS methods, making them prone to overfitting, which is particularly the case in sparse-view sce- narios

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T23:40:56.874659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-05T23:40:56.210358Z digest=sha256:861de47d6c59eb7340fca378c67640d5fdd4868dd7151c37bb13394c699d8f8b

Observation 18f942b0-583b-4094-9596-3d5211dfbe73 · outbound

This paper cites 2024; Zhang et al.

QFOR: A Fidelity-aware Orchestrator for Quantum Computing Environments using Deep Reinforcement Learning 2024; Zhang et al

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T23:40:56.649010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-05T23:40:56.420256Z digest=sha256:d75c571725baf9d32110bc790739c232438f48419b2258058988ccf5ade616c7

Observation 73ec29b1-4e99-4c85-abbe-39ac9df2fc36 · outbound

This paper cites UGOD: Uncertainty-Guided Differentiable Opacity and Soft Dropout for Enhanced Sparse-View 3DGS.

QFOR: A Fidelity-aware Orchestrator for Quantum Computing Environments using Deep Reinforcement Learning UGOD: Uncertainty-Guided Differentiable Opacity and Soft Dropout for Enhanced Sparse-View 3DGS

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-05T23:40:56.309221Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T23:40:56.309221Z digest=sha256:2a2154929c76c02b1a6d645ddba1b53cd641ac91851bd219eef0ad578ecbf25d

Pith citing papers

Observation 2166d7ff-a767-4601-8014-fe77fc05f9a7 · inbound

Hybrid Quantum-HPC Middleware Systems for Adaptive Resource, Workload and Task Management cites this paper.

Hybrid Quantum-HPC Middleware Systems for Adaptive Resource, Workload and Task Management QFOR: A Fidelity-aware Orchestrator for Quantum Computing Environments using Deep Reinforcement Learning

Reference 89

Resolution
verified exact
arxiv_id, observed 2026-05-13T18:53:08.451502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-13T18:48:58.904350Z digest=sha256:462ce5a31be1bdbb256ea98f2e61248fa646aa7feb6d19f7863859a5dbe3f246

Observation bb3645b8-901b-4880-bcfe-e593f32907be · inbound

Graph Reinforcement Learning for Calibration-Aware Quantum Circuit Routing cites this paper.

Graph Reinforcement Learning for Calibration-Aware Quantum Circuit Routing QFOR: A Fidelity-aware Orchestrator for Quantum Computing Environments using Deep Reinforcement Learning

Reference 20

Resolution
verified exact
arxiv_id, observed 2026-07-03T14:28:31.444698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-06-27T07:03:57.792425Z digest=sha256:e2b1adf47965d58c304bff04a7c03f8f0211510abc1cf9171a49b519eacf89d3

Observation b61753ff-9dbc-4227-bbe4-b8f1cd62c588 · inbound

Graph Reinforcement Learning for Calibration-Aware Quantum Circuit Routing cites this paper.

Graph Reinforcement Learning for Calibration-Aware Quantum Circuit Routing QFOR: A Fidelity-aware Orchestrator for Quantum Computing Environments using Deep Reinforcement Learning

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-02T11:45:33.499117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T11:45:33.499117Z digest=sha256:8a4555772dc35220873dc5c1ee0c92e9d52cea82a41a54701aff4df5b447d9bb