Paper Citation Record · LEDGER

Machine Learning for Practical Quantum Error Mitigation

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2309.17368.

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

pith.paper-citation-record.v1
2309.17368 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:04:01.655694Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T22:21:52.849898Z

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 b712d486-abd8-49ef-8fc1-648c9f2808f9 · inbound

Extension of Clifford Data Regression Methods for Quantum Error Mitigation cites this paper.

Extension of Clifford Data Regression Methods for Quantum Error Mitigation Machine Learning for Practical Quantum Error Mitigation

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-12T13:04:01.655694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 933cd0f5-5e66-4729-b9ab-c11afdb65f03 · inbound

Physics-inspired Machine Learning for Quantum Error Mitigation cites this paper.

Physics-inspired Machine Learning for Quantum Error Mitigation Machine Learning for Practical Quantum Error Mitigation

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-10T21:37:08.047329Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25f86a63-e3db-4f5b-9fcd-1b72962c5bc5 · inbound

The Role of Quantum Computing in Advancing Scientific High-Performance Computing: A perspective from the ADAC Institute cites this paper.

The Role of Quantum Computing in Advancing Scientific High-Performance Computing: A perspective from the ADAC Institute Machine Learning for Practical Quantum Error Mitigation

Reference 113

Resolution: verified exact; arxiv_id, observed 2026-05-18T22:21:52.852549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6fdc5fd1-3ffb-44a9-9d21-c63e6c722b91 · inbound

GSC-QEMit: A Telemetry-Driven Hierarchical Forecast-and-Bandit Framework for Adaptive Quantum Error Mitigation cites this paper.

GSC-QEMit: A Telemetry-Driven Hierarchical Forecast-and-Bandit Framework for Adaptive Quantum Error Mitigation Machine Learning for Practical Quantum Error Mitigation

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:51:23.087286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3ea4a0ad-7994-4815-8b8e-42532790a46b · inbound

The Virtuous Cycle of Quantum-Classical Machine Learning cites this paper.

The Virtuous Cycle of Quantum-Classical Machine Learning Machine Learning for Practical Quantum Error Mitigation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-14T10:48:19.458349Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b175e2b1-723c-4ea6-9225-c2b386c9d847 · inbound

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier cites this paper.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Machine Learning for Practical Quantum Error Mitigation

Reference 108

Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.404571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.