Pith. sign in

Paper Citation Record · LEDGER

Classically Approximating Variational Quantum Machine Learning with Random Fourier Features

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2210.13200.

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

pith.paper-citation-record.v1
2210.13200 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:14:58.618640Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

5
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 439ad5be-cbc4-4e49-8e04-42b639ea447b · inbound

Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers cites this paper.

Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers Classically Approximating Variational Quantum Machine Learning with Random Fourier Features

Reference 264

Resolution
unresolved
no resolver link, observed 2026-08-09T16:30:40.383485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T16:30:40.383485Z digest=sha256:09d93b7a6d5c48152f20ca6ae4f7e046faa161c99546f2a9790dc0abea592cae

Observation 79e8def4-c709-4cb1-8e9d-ac88cd45ca89 · inbound

Quantum Neural Networks for Cloud Cover Parameterizations in Climate Models cites this paper.

Quantum Neural Networks for Cloud Cover Parameterizations in Climate Models Classically Approximating Variational Quantum Machine Learning with Random Fourier Features

Reference 74

Resolution
unresolved
no resolver link, observed 2026-08-07T19:24:32.564867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T19:24:32.564867Z digest=sha256:187fa2dfe47968f4548b88abf348414f13af9b06c9850dbe77e9bd05dbd8d3de

Observation 85f00359-af4f-4462-bb37-85cf47b333a8 · inbound

Quantum reinforcement learning of classical rare dynamics: Enhancement by intrinsic Fourier features cites this paper.

Quantum reinforcement learning of classical rare dynamics: Enhancement by intrinsic Fourier features Classically Approximating Variational Quantum Machine Learning with Random Fourier Features

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-16T11:14:58.618640Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:14:58.618640Z digest=sha256:3dc3e26cded7dce3bf2c0c786b07165b0e1325a9a9bf126706caa0c3e5768418

Observation 613d6276-1ba0-4593-9690-44e1596aef9e · inbound

QFGN: A Quantum Approach to High-Fidelity Implicit Neural Representations cites this paper.

QFGN: A Quantum Approach to High-Fidelity Implicit Neural Representations Classically Approximating Variational Quantum Machine Learning with Random Fourier Features

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-16T10:08:29.241347Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T10:08:29.241347Z digest=sha256:830e7c7ab9cdd74d167787c04f7d0d467ff0dc83e6c016cd5bc8d7173fca2989

Observation f43905c3-91d5-4df0-9944-d2ca7946e572 · inbound

Out of Tune: Demystifying Noise-Effects on Quantum Fourier Models cites this paper.

Out of Tune: Demystifying Noise-Effects on Quantum Fourier Models Classically Approximating Variational Quantum Machine Learning with Random Fourier Features

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-07T04:53:36.495689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:53:36.495689Z digest=sha256:5e48cd377904975bb51e633e95760ca14e8bedd3fce260b06bd627b59389334c

Observation 7fcb6404-9138-4c5e-952f-39d15ee74e92 · inbound

Photonic processor benchmarking for variational quantum process tomography cites this paper.

Photonic processor benchmarking for variational quantum process tomography Classically Approximating Variational Quantum Machine Learning with Random Fourier Features

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-06T18:24:51.541098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:24:51.541098Z digest=sha256:885ca63dcfc5746e09181fa33dc6f82998283a51b8f567ce4aca513a684bb4d5

Observation 40a8edd6-126d-4f94-a534-3a3fdda86ac2 · inbound

Demonstration of Efficient Predictive Surrogates for Large-scale Quantum Processors cites this paper.

Demonstration of Efficient Predictive Surrogates for Large-scale Quantum Processors Classically Approximating Variational Quantum Machine Learning with Random Fourier Features

Reference 105

Resolution
unresolved
no resolver link, observed 2026-08-06T14:59:11.136457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T14:59:11.136457Z digest=sha256:9f4ad3ffd20ec44297432e308cd6a71f9fc68d0ad96037bcc0b05b00f16cd76b

Observation abd3b2c8-5205-4c67-9823-c969b284ccfb · inbound

Artificial intelligence for representing and characterizing quantum systems cites this paper.

Artificial intelligence for representing and characterizing quantum systems Classically Approximating Variational Quantum Machine Learning with Random Fourier Features

Reference 156

Resolution
unresolved
no resolver link, observed 2026-08-05T05:50:39.339661Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:50:39.339661Z digest=sha256:808b1807e7f4ec289a92e6da3289e2dd6992ab701dc4fbaebdc9f1508a907370

Observation b58fec7b-45e3-4de4-823b-b2a49fe72e78 · inbound

Coherent-State Propagation: A Computational Framework for Simulating Bosonic Quantum Systems cites this paper.

Coherent-State Propagation: A Computational Framework for Simulating Bosonic Quantum Systems Classically Approximating Variational Quantum Machine Learning with Random Fourier Features

Reference 253

Resolution
verified exact
arxiv_id, observed 2026-05-10T03:29:21.794953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-05-10T03:27:13.224597Z digest=sha256:2729f23e1efea8c2151a31ce615fbe6ba62b3c014787508c0cbdbf25d7d6976f

Observation 4fcfe76f-2d7f-478f-a9ec-69277b79bc8f · inbound

Beyond Gates: Pulse Level Quantum Fourier Models cites this paper.

Beyond Gates: Pulse Level Quantum Fourier Models Classically Approximating Variational Quantum Machine Learning with Random Fourier Features

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-05-08T20:24:09.304084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-08T16:21:01.344890Z digest=sha256:3a43ba79e8a7d4ab86cec904d5f763b36b1db5a7567129da641c844879c31674

Observation 73509177-4c2b-49d1-b485-4d4dfa2b4b60 · inbound

A Quantum-Classical Surrogate Model for the Collision Operator of the Lattice Boltzmann Method cites this paper.

A Quantum-Classical Surrogate Model for the Collision Operator of the Lattice Boltzmann Method Classically Approximating Variational Quantum Machine Learning with Random Fourier Features

Reference 43

Resolution
verified exact
arxiv_id, observed 2026-07-01T05:45:25.800151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-07-01T05:35:47.617882Z digest=sha256:d76aa13827537945fce664fb85e08bc305bbed5ab116716edb90771ac1f65f95