Paper Citation Record · LEDGER

Assessing Quantum Advantage for Gaussian Process Regression

As of 8 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2505.22502.

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

pith.paper-citation-record.v1
2505.22502 v2

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:15:37.597202Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T01:10:12.428509Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T01:10:12.644797Z

Reference resolution

46 of 46 outbound references displayed

verified exact3
verified fuzzy23
unresolved19
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1dab3da8-7032-4256-9b3f-b79d88605dce · outbound

This paper cites Quantum machine learning.

Assessing Quantum Advantage for Gaussian Process Regression Quantum machine learning

Reference 1

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Source-reported events for the cited work

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Observation 3b5f0d62-9a10-4309-aeb3-95e2f455d106 · outbound

This paper cites Cerezo, Guillaume Verdon, Hsin-Yuan Huang, Lukasz Cincio, and Patrick J.

Assessing Quantum Advantage for Gaussian Process Regression Cerezo, Guillaume Verdon, Hsin-Yuan Huang, Lukasz Cincio, and Patrick J

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:56.362925Z

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Observation 6a3de4f6-6dee-4ef5-adc0-acf79146f88e · outbound

This paper cites Quantum machine learning: a classical perspective.

Assessing Quantum Advantage for Gaussian Process Regression Quantum machine learning: a classical perspective

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:56.218363Z

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Observation f8a928d9-3cc7-4fd4-a9ac-fdbc49cb4ea4 · outbound

This paper cites Quantum algorithms for supervised and unsupervised machine learning.

Assessing Quantum Advantage for Gaussian Process Regression Quantum algorithms for supervised and unsupervised machine learning

Reference 4

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Observation 58d8b3cb-2943-4b33-918c-bb3b8fb2788d · outbound

This paper cites Quantum principal compo- nent analysis.

Assessing Quantum Advantage for Gaussian Process Regression Quantum principal compo- nent analysis

Reference 5

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Observation b45f5b9c-2c32-48b1-8d57-25b39683d237 · outbound

This paper cites Quantum Recommendation Systems.

Assessing Quantum Advantage for Gaussian Process Regression Quantum Recommendation Systems

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:55.960941Z

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Observation e877ed34-19d8-4b12-a991-01b8249f412c · outbound

This paper cites Quantum algorithm for solving linear systems of equations.

Assessing Quantum Advantage for Gaussian Process Regression Quantum algorithm for solving linear systems of equations

Reference 7

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Source-reported events for the cited work

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Observation a4895fd3-5fcf-496d-aa41-eac68330a33b · outbound

This paper cites an unresolved cited work.

Assessing Quantum Advantage for Gaussian Process Regression Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:15:55.791015Z

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Observation cd1de0c6-c26a-48c2-81db-1a626ac5c75c · outbound

This paper cites A quantum-inspired classical algorithm for recommendation systems, May.

Assessing Quantum Advantage for Gaussian Process Regression A quantum-inspired classical algorithm for recommendation systems, May

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:55.629996Z

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Observation 7f9ee1ad-3a97-4736-bb82-283608332630 · outbound

This paper cites Quantum principal component analysis only achieves an exponential speedup because of its state preparation assumptions.

Assessing Quantum Advantage for Gaussian Process Regression Quantum principal component analysis only achieves an exponential speedup because of its state preparation assumptions

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:15:38.679043Z

Source-reported events for the cited work

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Observation 9cfc6851-24ba-45ad-9278-5c9348fefd9b · outbound

This paper cites an unresolved cited work.

Assessing Quantum Advantage for Gaussian Process Regression Unresolved cited work

Reference 11

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Observation c06d3364-e46c-4467-a4c4-3b896b17a351 · outbound

This paper cites Fitzsimons, and Joseph F.

Assessing Quantum Advantage for Gaussian Process Regression Fitzsimons, and Joseph F

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:55.329589Z

Source-reported events for the cited work

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Observation 4c16720a-91a3-4448-a75f-24632945e934 · outbound

This paper cites Quantum algorithm for Gaussian process regression.

Assessing Quantum Advantage for Gaussian Process Regression Quantum algorithm for Gaussian process regression

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:55.214514Z

Source-reported events for the cited work

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Observation a2a1b793-e797-4a65-a6e1-d9170514d3e6 · outbound

This paper cites Galvis-Florez, and Simo S¨ arkk¨ a.

Assessing Quantum Advantage for Gaussian Process Regression Galvis-Florez, and Simo S¨ arkk¨ a

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:55.017638Z

Source-reported events for the cited work

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Observation 4bd7830a-a592-4cf0-b433-096171f656c3 · outbound

This paper cites An introduction to the conjugate gradient method with- out the agonizing pain, 1994.

Assessing Quantum Advantage for Gaussian Process Regression An introduction to the conjugate gradient method with- out the agonizing pain, 1994

Reference 15

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Source-reported events for the cited work

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Observation 2e72ca44-6e94-4b7b-8685-36ea35d846e4 · outbound

This paper cites Faster quantum-inspired algorithms for solving linear systems.

Assessing Quantum Advantage for Gaussian Process Regression Faster quantum-inspired algorithms for solving linear systems

Reference 16

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Observation 8bf17589-bc23-4491-92aa-5322077e9c2f · outbound

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Assessing Quantum Advantage for Gaussian Process Regression Unresolved cited work

Reference 17

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Source-reported events for the cited work

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Observation 7e1b8820-c0c2-43b4-983b-2e5c161c3506 · outbound

This paper cites Eigenvalues and Condition Numbers of Random Matrices.

Assessing Quantum Advantage for Gaussian Process Regression Eigenvalues and Condition Numbers of Random Matrices

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:54.546347Z

Source-reported events for the cited work

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Observation cf0933b0-9402-46c6-b904-f4c707b6a4e7 · outbound

This paper cites The condition number of a randomly perturbed matrix.

Assessing Quantum Advantage for Gaussian Process Regression The condition number of a randomly perturbed matrix

Reference 19

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Source-reported events for the cited work

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Observation 3cf7434e-30b4-44c8-a7ff-305e0c4d64d2 · outbound

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Assessing Quantum Advantage for Gaussian Process Regression Unresolved cited work

Reference 20

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Observation bb5dde40-225d-4153-9da6-7d3d73b204ec · outbound

This paper cites Zimmermann.

Assessing Quantum Advantage for Gaussian Process Regression Zimmermann

Reference 21

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Observation e971260f-b4a1-4d29-a48c-095bbca88904 · outbound

This paper cites Chaitin-Chatelin.

Assessing Quantum Advantage for Gaussian Process Regression Chaitin-Chatelin

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:54.078803Z

Source-reported events for the cited work

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Observation 8783eb95-7da5-416e-b0e3-b54ff6cfe292 · outbound

This paper cites Dunford and J.T.

Assessing Quantum Advantage for Gaussian Process Regression Dunford and J.T

Reference 23

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Observation d4852ed5-4ace-4834-8ba6-ae97cb3cc004 · outbound

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Assessing Quantum Advantage for Gaussian Process Regression Unresolved cited work

Reference 24

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Observation 5025e20e-a758-4a24-b2ad-b033b60c848e · outbound

This paper cites A Survey on Graph Kernels.

Assessing Quantum Advantage for Gaussian Process Regression A Survey on Graph Kernels

Reference 25

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Observation 652f0c8b-7994-4fdd-b094-5eb25a042288 · outbound

This paper cites String kernels construction and fusion: a survey with bioinformatics application.

Assessing Quantum Advantage for Gaussian Process Regression String kernels construction and fusion: a survey with bioinformatics application

Reference 26

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Observation 36bfbc07-5182-459f-8f3c-472f805d1ec7 · outbound

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Assessing Quantum Advantage for Gaussian Process Regression Unresolved cited work

Reference 27

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Observation d63df0d0-b3c3-420c-8f21-20910cee24db · outbound

This paper cites an unresolved cited work.

Assessing Quantum Advantage for Gaussian Process Regression Unresolved cited work

Reference 28

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Source-reported events for the cited work

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Observation 7382bb3e-cee2-45a7-ab1d-07b58a198e9e · outbound

This paper cites Hilbert space methods for reduced-rank Gaussian process regression.

Assessing Quantum Advantage for Gaussian Process Regression Hilbert space methods for reduced-rank Gaussian process regression

Reference 29

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Observation 7a4a012f-ca3b-4ad6-a888-983b9c00b295 · outbound

This paper cites When Gaussian Process Meets Big Data: A Review of Scalable GPs.

Assessing Quantum Advantage for Gaussian Process Regression When Gaussian Process Meets Big Data: A Review of Scalable GPs

Reference 30

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Source-reported events for the cited work

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Observation 109052b6-5143-41c0-b5d0-ca15917c1e47 · outbound

This paper cites Qubit-Efficient Randomized Quantum Algorithms for Linear Algebra.

Assessing Quantum Advantage for Gaussian Process Regression Qubit-Efficient Randomized Quantum Algorithms for Linear Algebra

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:40.804351Z

Source-reported events for the cited work

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Observation 96d92622-f47a-48e1-aa68-b461bc30815c · outbound

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Assessing Quantum Advantage for Gaussian Process Regression Unresolved cited work

Reference 32

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Observation 33aa58a7-9c52-42a5-bfc0-80c5250b5bc5 · outbound

This paper cites Quantum circulant preconditioner for a linear system of equations.

Assessing Quantum Advantage for Gaussian Process Regression Quantum circulant preconditioner for a linear system of equations

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:40.446432Z

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Observation 29a206cc-39be-4393-8492-dfa1f39e7359 · outbound

This paper cites Preconditioning for Scalable Gaussian Process Hyperparameter Optimization.

Assessing Quantum Advantage for Gaussian Process Regression Preconditioning for Scalable Gaussian Process Hyperparameter Optimization

Reference 34

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Source-reported events for the cited work

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Observation 1fc79f8c-42e2-4006-9c4c-78dbb2a8dbb7 · outbound

This paper cites Quantum Support Vector Ma- chine for Big Data Classification.

Assessing Quantum Advantage for Gaussian Process Regression Quantum Support Vector Ma- chine for Big Data Classification

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:40.316533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e629e42a-754f-4f2f-9c8c-92db3f499e1f · outbound

This paper cites C´ orcoles, Kristan Temme, Aram W.

Assessing Quantum Advantage for Gaussian Process Regression C´ orcoles, Kristan Temme, Aram W

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:39.995273Z

Source-reported events for the cited work

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Observation 48ae4340-2f89-4145-b832-5aceb8728624 · outbound

This paper cites Quantum Machine Learning in Feature Hilbert Spaces.

Assessing Quantum Advantage for Gaussian Process Regression Quantum Machine Learning in Feature Hilbert Spaces

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:39.837269Z

Source-reported events for the cited work

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Observation 3fcddc55-da66-4c9c-a005-ad1ed1e35e23 · outbound

This paper cites A rigorous and robust quantum speed-up in supervised machine learning.

Assessing Quantum Advantage for Gaussian Process Regression A rigorous and robust quantum speed-up in supervised machine learning

Reference 38

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Source-reported events for the cited work

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Observation 00a856c8-0409-4e37-b874-1a3ae67b3bfa · outbound

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Assessing Quantum Advantage for Gaussian Process Regression Unresolved cited work

Reference 39

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Observation 26098587-6591-4937-b6dc-06953e5fa1f2 · outbound

This paper cites Neural Tangent Kernel: Convergence and Generalization in Neural Networks.

Assessing Quantum Advantage for Gaussian Process Regression Neural Tangent Kernel: Convergence and Generalization in Neural Networks

Reference 40

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Source-reported events for the cited work

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Observation 211ee424-5b2c-40b0-a697-b066df62bbae · outbound

This paper cites Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent.

Assessing Quantum Advantage for Gaussian Process Regression Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent

Reference 41

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Source-reported events for the cited work

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Observation 595179f1-af74-4138-aa6d-58e71585a684 · outbound

This paper cites Gradient Descent Provably Optimizes Over-parameterized Neural Networks.

Assessing Quantum Advantage for Gaussian Process Regression Gradient Descent Provably Optimizes Over-parameterized Neural Networks

Reference 42

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Source-reported events for the cited work

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Observation 622d69f6-0cc2-4b5e-ad92-5c650fec9015 · outbound

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Assessing Quantum Advantage for Gaussian Process Regression Unresolved cited work

Reference 43

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:15:39.405150Z

Source-reported events for the cited work

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Observation df29c00c-ec11-4df7-8936-f5ccf8d15f02 · outbound

This paper cites Random matrix approximation of spectra of integral operators.

Assessing Quantum Advantage for Gaussian Process Regression Random matrix approximation of spectra of integral operators

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:39.180740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 35acd12a-fa66-40a5-a87c-0306fb331df1 · outbound

This paper cites A very short proof of cauchy’s interlace theorem for eigenvalues of hermitian matrices, 2005.

Assessing Quantum Advantage for Gaussian Process Regression A very short proof of cauchy’s interlace theorem for eigenvalues of hermitian matrices, 2005

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:38.926372Z

Source-reported events for the cited work

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Observation c9fec235-a543-44a6-a97c-bcb95f6bf1a1 · outbound

This paper cites an unresolved cited work.

Assessing Quantum Advantage for Gaussian Process Regression Unresolved cited work

Reference 2014

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:15:40.138855Z

Source-reported events for the cited work

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Pith citing papers

Observation 78c1fe1e-426a-43cc-aedf-b521e2046091 · inbound

Spectral Estimation with Free Decompression cites this paper.

Spectral Estimation with Free Decompression Assessing Quantum Advantage for Gaussian Process Regression

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-08-07T01:10:12.650902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.