Paper Citation Record · LEDGER

Physics-Informed Quantum Machine Learning: Solving nonlinear differential equations in latent spaces without costly grid evaluations

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2308.01827.

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

pith.paper-citation-record.v1
2308.01827 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 2026-08-07T06:34:17.273281+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:12:54.022909Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T21:36:15.728740Z

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 4ce43baa-6a9c-46bc-8543-5110cb896c6e · inbound

Robust quantum reservoir computers for forecasting chaotic dynamics: generalized synchronization and stability cites this paper.

Robust quantum reservoir computers for forecasting chaotic dynamics: generalized synchronization and stability Physics-Informed Quantum Machine Learning: Solving nonlinear differential equations in latent spaces without costly grid evaluations

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T22:12:54.022909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 213801fa-ca4a-40d3-a04b-e18bd9aac187 · inbound

Partitioned Hybrid Quantum Fourier Neural Operators for Scientific Quantum Machine Learning cites this paper.

Partitioned Hybrid Quantum Fourier Neural Operators for Scientific Quantum Machine Learning Physics-Informed Quantum Machine Learning: Solving nonlinear differential equations in latent spaces without costly grid evaluations

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T18:19:59.094317Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0af394e8-d2ec-4d9a-9aa0-76f0350e7888 · inbound

Variational Quantum Physics-Informed Neural Networks for Hydrological PDE-Constrained Learning with Inherent Uncertainty Quantification cites this paper.

Variational Quantum Physics-Informed Neural Networks for Hydrological PDE-Constrained Learning with Inherent Uncertainty Quantification Physics-Informed Quantum Machine Learning: Solving nonlinear differential equations in latent spaces without costly grid evaluations

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:06:16.475428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 045278ed-37d0-4016-b4ed-c4f52fad78f3 · inbound

Q-SINDy: Quantum-Kernel Sparse Identification of Nonlinear Dynamics with Provable Coefficient Debiasing cites this paper.

Q-SINDy: Quantum-Kernel Sparse Identification of Nonlinear Dynamics with Provable Coefficient Debiasing Physics-Informed Quantum Machine Learning: Solving nonlinear differential equations in latent spaces without costly grid evaluations

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-10T07:47:12.578936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c258c078-8586-49db-9b16-32ea96cab7c0 · inbound

Efficient and Expressive Boundary Conditions in Quantum Lattice Boltzmann Methods cites this paper.

Efficient and Expressive Boundary Conditions in Quantum Lattice Boltzmann Methods Physics-Informed Quantum Machine Learning: Solving nonlinear differential equations in latent spaces without costly grid evaluations

Reference 27

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T21:36:15.730574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.