Paper Citation Record · LEDGER

Dimensionality Reduction of QAOA Parameter Space with Kernel PCA for Max-Cut

As of 20 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2606.23718.

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

pith.paper-citation-record.v1
2606.23718 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-26T21:07:48.052605Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

26 of 26 outbound references displayed

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verified fuzzy0
unresolved23
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9dd9436b-38d6-40ca-b04d-f25ebf9ce299 · outbound

This paper cites Quantum optimization for real-world applications: A survey.

Dimensionality Reduction of QAOA Parameter Space with Kernel PCA for Max-Cut Quantum optimization for real-world applications: A survey

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-26T21:07:48.052605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c214ebf2-fe84-40c0-b238-effe7006b891 · outbound

This paper cites Applications of quantum algorithms to combinatorial optimization.

Dimensionality Reduction of QAOA Parameter Space with Kernel PCA for Max-Cut Applications of quantum algorithms to combinatorial optimization

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-26T21:07:48.052605Z

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Observation b827b978-14f5-4dbb-acd6-79ee31a2cb75 · outbound

This paper cites an unresolved cited work.

Dimensionality Reduction of QAOA Parameter Space with Kernel PCA for Max-Cut Unresolved cited work

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-26T21:07:48.052605Z

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Observation a60bcdc2-4b12-4b4e-9863-45e0a013c23a · outbound

This paper cites an unresolved cited work.

Dimensionality Reduction of QAOA Parameter Space with Kernel PCA for Max-Cut Unresolved cited work

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-26T21:07:48.052605Z

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Observation f8712b94-b631-4978-9a02-61015999809f · outbound

This paper cites Goemans and David P.

Dimensionality Reduction of QAOA Parameter Space with Kernel PCA for Max-Cut Goemans and David P

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-26T21:07:48.052605Z

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Observation 6f1ec179-eb09-435a-9da2-e11254a8bd0b · outbound

This paper cites A Quantum Approximate Optimization Algorithm.

Dimensionality Reduction of QAOA Parameter Space with Kernel PCA for Max-Cut A Quantum Approximate Optimization Algorithm

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-06-26T21:09:57.509967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ba3da17c-44c3-45d2-bbb7-cd160cd741a1 · outbound

This paper cites Quantum computing in the nisq era and beyond.Quantum, 2:79, 2018.

Dimensionality Reduction of QAOA Parameter Space with Kernel PCA for Max-Cut Quantum computing in the nisq era and beyond.Quantum, 2:79, 2018

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-26T21:07:48.052605Z

Source-reported events for the cited work

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Observation 97f8e68f-150a-4140-8339-6c90eb5a1dbf · outbound

This paper cites General parameter- shift rules for quantum gradients.Quantum, 6:670, 2022.

Dimensionality Reduction of QAOA Parameter Space with Kernel PCA for Max-Cut General parameter- shift rules for quantum gradients.Quantum, 6:670, 2022

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-26T21:07:48.052605Z

Source-reported events for the cited work

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Observation e26c80c0-7236-4d99-89e5-c150f61ae947 · outbound

This paper cites Akshay, D.

Dimensionality Reduction of QAOA Parameter Space with Kernel PCA for Max-Cut Akshay, D

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-26T21:07:48.052605Z

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Observation 6d9eef47-0adb-414a-a24b-8739598940a5 · outbound

This paper cites Transferability of graph neural networks using graphon neural networks with generalization guarantees.

Dimensionality Reduction of QAOA Parameter Space with Kernel PCA for Max-Cut Transferability of graph neural networks using graphon neural networks with generalization guarantees

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-26T21:07:48.052605Z

Source-reported events for the cited work

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Observation 872b6d6c-babf-46cb-ab96-0279324bdb3a · outbound

This paper cites Akshay, et al.

Dimensionality Reduction of QAOA Parameter Space with Kernel PCA for Max-Cut Akshay, et al

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-26T21:07:48.052605Z

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Observation 32adf37a-8aaa-44a3-9b34-015134c9adf8 · outbound

This paper cites Obstacles to variational quantum optimization from symmetry protection.Nature Physics, 17: 452–456, 2021.

Dimensionality Reduction of QAOA Parameter Space with Kernel PCA for Max-Cut Obstacles to variational quantum optimization from symmetry protection.Nature Physics, 17: 452–456, 2021

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-26T21:07:48.052605Z

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Observation 55b25275-55f0-4d21-9d1d-cb873853f8c7 · outbound

This paper cites Qaoa-pca: Enhancing efficiency in the quantum approximate optimization algorithm via principal component analysis.

Dimensionality Reduction of QAOA Parameter Space with Kernel PCA for Max-Cut Qaoa-pca: Enhancing efficiency in the quantum approximate optimization algorithm via principal component analysis

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-26T21:07:48.052605Z

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Observation eeedddce-c86e-4cdf-b614-7e5b78ce5f73 · outbound

This paper cites Exploiting symmetry reduces the cost of training qaoa.IEEE Transactions on Quantum Engineering, 2:1–24, 2021.

Dimensionality Reduction of QAOA Parameter Space with Kernel PCA for Max-Cut Exploiting symmetry reduces the cost of training qaoa.IEEE Transactions on Quantum Engineering, 2:1–24, 2021

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-26T21:07:48.052605Z

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Observation 6a278366-9334-473b-980a-fbecc6d50e30 · outbound

This paper cites Nakanishi, Keisuke Fujii, and Seiichiro Todo.

Dimensionality Reduction of QAOA Parameter Space with Kernel PCA for Max-Cut Nakanishi, Keisuke Fujii, and Seiichiro Todo

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-26T21:07:48.052605Z

Source-reported events for the cited work

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Observation 2182bb42-d33b-4553-b1f1-b54aca83db83 · outbound

This paper cites Nonlinear component analysis as a kernel eigenvalue problem.Neural Computation, 10:1299–1319, 1998.

Dimensionality Reduction of QAOA Parameter Space with Kernel PCA for Max-Cut Nonlinear component analysis as a kernel eigenvalue problem.Neural Computation, 10:1299–1319, 1998

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-26T21:07:48.052605Z

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Observation d6374ad8-b12b-4afb-bdc3-acaf84eebbc6 · outbound

This paper cites The quantum approximate optimization algorithm and the sherrington-kirkpatrick model at infinite size.Quantum, 6:863, 2022.

Dimensionality Reduction of QAOA Parameter Space with Kernel PCA for Max-Cut The quantum approximate optimization algorithm and the sherrington-kirkpatrick model at infinite size.Quantum, 6:863, 2022

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-26T21:07:48.052605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68968506-1242-4eac-964f-48db64b488d0 · outbound

This paper cites Limitations of optimization algorithms on noisy quantum devices.

Dimensionality Reduction of QAOA Parameter Space with Kernel PCA for Max-Cut Limitations of optimization algorithms on noisy quantum devices

Reference 18

Resolution: verified exact; doi, observed 2026-06-26T21:09:57.505147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 37a0a580-a56d-4dde-bf2f-aaa4005e4d90 · outbound

This paper cites On the influence of the kernel on the consistency of support vector machines.Journal of Machine Learning Research, 2:67–93, 2001.

Dimensionality Reduction of QAOA Parameter Space with Kernel PCA for Max-Cut On the influence of the kernel on the consistency of support vector machines.Journal of Machine Learning Research, 2:67–93, 2001

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-26T21:07:48.052605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c199becd-c9ec-42c1-8d9c-6687035d9720 · outbound

This paper cites Functions of positive and negative type and their connection with the theory of integral equations.Philosophical Transactions of the Royal Society A, 209: 415–446, 1909.

Dimensionality Reduction of QAOA Parameter Space with Kernel PCA for Max-Cut Functions of positive and negative type and their connection with the theory of integral equations.Philosophical Transactions of the Royal Society A, 209: 415–446, 1909

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-26T21:07:48.052605Z

Source-reported events for the cited work

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Observation b588f432-87fb-4909-856d-4c2a90fececa · outbound

This paper cites Borgwardt, Malte J.

Dimensionality Reduction of QAOA Parameter Space with Kernel PCA for Max-Cut Borgwardt, Malte J

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-26T21:07:48.052605Z

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Observation 7a308cde-b78e-4b6a-82e3-ed4cb5e73bd9 · outbound

This paper cites Bakir, Jason Weston, and Bernhard Sch¨ olkopf.

Dimensionality Reduction of QAOA Parameter Space with Kernel PCA for Max-Cut Bakir, Jason Weston, and Bernhard Sch¨ olkopf

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-26T21:07:48.052605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 430d1771-126d-4891-9efe-d61546ab93d9 · outbound

This paper cites On random graphs.Publicationes Mathematicae, 6: 290–297, 1959.

Dimensionality Reduction of QAOA Parameter Space with Kernel PCA for Max-Cut On random graphs.Publicationes Mathematicae, 6: 290–297, 1959

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-26T21:07:48.052605Z

Source-reported events for the cited work

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Observation 9c6de450-afca-47e5-beab-710d430afdbf · outbound

This paper cites Emergence of scaling in random networks.

Dimensionality Reduction of QAOA Parameter Space with Kernel PCA for Max-Cut Emergence of scaling in random networks

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-26T21:07:48.052605Z

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Observation e51f75b1-f2d2-4707-bc7d-340f452a4781 · outbound

This paper cites Watts and Steven H.

Dimensionality Reduction of QAOA Parameter Space with Kernel PCA for Max-Cut Watts and Steven H

Reference 25

Resolution: unresolved; no resolver link, observed 2026-06-26T21:07:48.052605Z

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Unavailable: canonical work link unavailable.

Observation b5098889-1389-44a1-8557-55be827b084f · outbound

This paper cites Qiskit: An Open-source Framework for Quantum Computing.

Dimensionality Reduction of QAOA Parameter Space with Kernel PCA for Max-Cut Qiskit: An Open-source Framework for Quantum Computing

Reference 26

Resolution: verified exact; doi, observed 2026-06-26T21:09:57.512321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

No inbound Pith citation observations are available.