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Paper Citation Record · LEDGER

Meta-learning characteristics and dynamics of quantum systems

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2503.10492.

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

pith.paper-citation-record.v1
2503.10492 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:23:50.313529Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T03:26:29.421662Z

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 78da9b39-c232-474b-b82a-e37e8a3fcc98 · inbound

Automated All-RF Tuning for Spin Qubit Readout and Control cites this paper.

Automated All-RF Tuning for Spin Qubit Readout and Control Meta-learning characteristics and dynamics of quantum systems

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-07T04:23:50.313529Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:23:50.313529Z digest=sha256:8be22155c1ac4e99e407dbccc91b180c6de227c1dac249a0a521256ecaba8619

Observation 4c09af7d-a1ce-4374-b572-3ecddf2428c3 · inbound

Inverse Physics-informed neural networks procedure for detecting noise in open quantum systems cites this paper.

Inverse Physics-informed neural networks procedure for detecting noise in open quantum systems Meta-learning characteristics and dynamics of quantum systems

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-06T16:48:35.437836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:48:35.437836Z digest=sha256:5dae6d9d3cd57df3b84452274e1130150701c0f68cffefd11490654378122deb

Observation fec0cd69-a1b6-4b31-853f-5af3fa67ad9c · inbound

Data-Driven Hamiltonian Reduction for Superconducting Qubits via Meta-Learning cites this paper.

Data-Driven Hamiltonian Reduction for Superconducting Qubits via Meta-Learning Meta-learning characteristics and dynamics of quantum systems

Reference 26

Resolution
verified exact
arxiv_id, observed 2026-05-11T21:51:30.143654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-08T04:02:57.315065Z digest=sha256:b84614ecb2a74398c3992fde9295b21e0a794c52368ba9baf009ff24f2c073eb

Observation ceb2d250-d0a7-4ab4-9ec8-a739a7d00a9b · inbound

RIZZ: Routing Interactions to Near Zero-Interference Zones for Continual Adaptation of Black-Box Agents cites this paper.

RIZZ: Routing Interactions to Near Zero-Interference Zones for Continual Adaptation of Black-Box Agents Meta-learning characteristics and dynamics of quantum systems

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-07-02T03:26:29.423378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-06-28T10:03:49.270726Z digest=sha256:0943e8dd3ef538b874e588ca0c361ce23fd4da0c58c95c2c090fb766f74f4896

Observation 61ee42ce-705b-4be6-b367-fea8e1351215 · inbound

Action-Factored Multi-Agent Reinforcement Learning for Scalable Quantum Device Tuning cites this paper.

Action-Factored Multi-Agent Reinforcement Learning for Scalable Quantum Device Tuning Meta-learning characteristics and dynamics of quantum systems

Reference 15

Resolution
unresolved
no resolver link, observed 2026-07-13T03:08:01.590659Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T03:08:01.590659Z digest=sha256:5fa0afe547bc03624ef5e2a5d249fd1bbffa7ad73336207ff3f4d4e88253c0f6