Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential | Paper Citation Record

Observation 68621b71-7ae1-4ca4-b9ff-da42e36b1f88 · outbound

This paper cites Challenges and opportunities in quantum optimization.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Challenges and opportunities in quantum optimization

Reference 1

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Observation 446e04d7-d9d9-4be1-b190-d872cce4af33 · outbound

This paper cites Aizerman, E.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Aizerman, E

Reference 2

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Observation 29cf88a6-3611-44d5-bfeb-774f5c7a9734 · outbound

This paper cites Quantum feature maps for graph machine learning on a neutral atom quantum processor.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Quantum feature maps for graph machine learning on a neutral atom quantum processor

Reference 3

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Observation e531788f-708d-42e5-bdda-367a7c513141 · outbound

This paper cites Kernels for vector-valued functions: A review.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Kernels for vector-valued functions: A review

Reference 4

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This paper cites Aronszajn.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Aronszajn

Reference 5

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Observation 9cf243da-dae5-4a24-a4d4-22d38f30dfe9 · outbound

This paper cites A quantum jensen--shannon graph kernel for unattributed graphs.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential A quantum jensen--shannon graph kernel for unattributed graphs

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T11:03:04.628536Z

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Observation cdfa82b9-f622-4206-9d9c-efa1d26df02e · outbound

This paper cites Qbmk: Quantum-based matching kernels for un-attributed graphs.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Qbmk: Quantum-based matching kernels for un-attributed graphs

Reference 7

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Observation a592e09d-b755-4f33-85ce-9966b152a388 · outbound

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Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Unresolved cited work

Reference 8

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Observation 7c86aecf-857e-41e9-a601-eba1df411a9d · outbound

This paper cites Benign overfitting in linear regression.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Benign overfitting in linear regression

Reference 9

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Observation 72721db7-9a0e-48ad-bb60-8217e0d00777 · outbound

This paper cites Noncommutative christoffel-darboux kernels.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Noncommutative christoffel-darboux kernels

Reference 10

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Observation 8cbf4060-5070-46a6-8438-88e08cd71f02 · outbound

This paper cites Reconciling modern machine-learning practice and the classical bias--variance trade-off.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Reconciling modern machine-learning practice and the classical bias--variance trade-off

Reference 11

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Observation 78985f60-825c-4d3f-bcf6-797080049dad · outbound

This paper cites Quantum machine learning.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Quantum machine learning

Reference 12

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Observation b98e5e31-25cf-4390-99da-49b616a25b2f · outbound

This paper cites Quantum classifier with tailored quantum kernel.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Quantum classifier with tailored quantum kernel

Reference 13

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Observation f75de162-07b7-4f34-87ec-4e87fee2ea10 · outbound

This paper cites Graph kernels: State-of-the-art and future challenges.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Graph kernels: State-of-the-art and future challenges

Reference 14

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Observation c382dd5b-6e6e-4b82-a0ad-d9abebdff376 · outbound

This paper cites an unresolved cited work.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Unresolved cited work

Reference 15

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Observation 6bca206c-66bd-483c-bbc8-3dae30c9c933 · outbound

This paper cites Input output kernel regression: Supervised and semi-supervised structured output prediction with operator-valued kernels.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Input output kernel regression: Supervised and semi-supervised structured output prediction with operator-valued kernels

Reference 16

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Observation 63904590-b581-43b1-8831-e61734bd17d3 · outbound

This paper cites C*-Algebras and Mathematical Foundations of Quantum Statistical Mechanics: An Introduction.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential C*-Algebras and Mathematical Foundations of Quantum Statistical Mechanics: An Introduction

Reference 17

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Observation f549403c-bc60-4f80-9a77-bf45e7353bbf · outbound

This paper cites Quantum fingerprinting.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Quantum fingerprinting

Reference 18

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Observation 44c74ec5-8412-4fd1-b828-47491fe4e556 · outbound

This paper cites Optimization of polynomials in non-commuting variables, volume 2.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Optimization of polynomials in non-commuting variables, volume 2

Reference 19

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Observation b6a61274-fe2f-46c9-913c-4b9a81269c2e · outbound

This paper cites Towards a theory of non-commutative optimization: Geodesic 1st and 2nd order methods for moment maps and polytopes.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Towards a theory of non-commutative optimization: Geodesic 1st and 2nd order methods for moment maps and polytopes

Reference 20

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Observation 60e8cf7f-ef9a-4763-be46-9b77d0fd1c01 · outbound

This paper cites Wild, and Ruslan Shaydulin.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Wild, and Ruslan Shaydulin

Reference 21

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Observation 2daa10ca-ddfb-410f-bb09-45513b3075a6 · outbound

This paper cites Universal multi-task kernels.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Universal multi-task kernels

Reference 22

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Observation 60af1183-61b4-4689-8fed-89beb5bf46ea · outbound

This paper cites Vector valued reproducing kernel hilbert spaces and universality.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Vector valued reproducing kernel hilbert spaces and universality

Reference 23

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Observation cc2c150b-1e25-42c5-b008-748a183d334a · outbound

This paper cites Quantum machine learning: a classical perspective.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Quantum machine learning: a classical perspective

Reference 24

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Observation ac43b313-00e7-4721-88f3-71e38a84ddc0 · outbound

This paper cites Quantum Computing: Lecture Notes.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Quantum Computing: Lecture Notes

Reference 25

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Observation 7ae1cd1a-55eb-48e6-b62d-112c0314ac46 · outbound

This paper cites Quantum advantage seeker with kernels (quask): a software framework to speed up the research in quantum machine learning.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Quantum advantage seeker with kernels (quask): a software framework to speed up the research in quantum machine learning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:06.028833Z

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Observation 41166919-2984-43d3-afbf-25d2b7b085f7 · outbound

This paper cites Machine learning & artificial intelligence in the quantum domain: a review of recent progress.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Machine learning & artificial intelligence in the quantum domain: a review of recent progress

Reference 27

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Observation 02b0b5df-ea85-4560-8280-9379b1581117 · outbound

This paper cites Deep sketched output kernel regression for structured prediction.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Deep sketched output kernel regression for structured prediction

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:06.000813Z

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Observation 4a270a44-6727-460a-84ce-fe796d76bcbb · outbound

This paper cites Learning multiple tasks with kernel methods.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Learning multiple tasks with kernel methods

Reference 29

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Observation e6e46b0a-18ae-42f1-8a86-a9907c5d7c46 · outbound

This paper cites Quantum channel identification problem.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Quantum channel identification problem

Reference 30

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Observation 1e20cf47-ada2-4662-ba76-4f17e40675c2 · outbound

This paper cites Quantum parameter estimation of a generalized pauli channel.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Quantum parameter estimation of a generalized pauli channel

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.960899Z

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Observation fb960e86-4458-48b2-a521-522b13c397d1 · outbound

This paper cites Learning quantum systems.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Learning quantum systems

Reference 32

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Observation bddd6d44-5ed6-45bd-81fe-c039b56a4358 · outbound

This paper cites Quantum kernel alignment with stochastic gradient descent.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Quantum kernel alignment with stochastic gradient descent

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.933767Z

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Observation e164681d-7589-4aed-b155-2cf4e781ce9c · outbound

This paper cites Kernelizing the output of tree-based methods.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Kernelizing the output of tree-based methods

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.919706Z

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Observation dd0c7ef6-9872-4fff-a788-cc9b9c63e053 · outbound

This paper cites Understanding quantum machine learning also requires rethinking generalization.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Understanding quantum machine learning also requires rethinking generalization

Reference 35

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Observation f614e049-f77d-4968-b489-85100811b1b0 · outbound

This paper cites Covariant quantum kernels for data with group structure.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Covariant quantum kernels for data with group structure

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.891712Z

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Observation bba0469b-780b-43c7-a834-7029117bce55 · outbound

This paper cites Bounds on entanglement dimensions and quantum graph parameters via noncommutative polynomial optimization.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Bounds on entanglement dimensions and quantum graph parameters via noncommutative polynomial optimization

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.877919Z

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Observation 25e14557-e298-4eb8-9630-87745863e2e5 · outbound

This paper cites A fast quantum mechanical algorithm for database search.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential A fast quantum mechanical algorithm for database search

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.863313Z

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Observation 46286512-f72f-41c0-b590-16c4542cdcf0 · outbound

This paper cites Reproducing kernel hilbert c*-module and kernel mean embeddings.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Reproducing kernel hilbert c*-module and kernel mean embeddings

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.848591Z

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Observation 6090b34f-4c32-40b2-a465-8ce7d62ee25b · outbound

This paper cites Deep learning with kernels through rkhm and the perron-frobenius operator.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Deep learning with kernels through rkhm and the perron-frobenius operator

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.834515Z

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Observation 4cc03201-53b6-4e06-a451-1023a538eae0 · outbound

This paper cites Learning in rkhm: a c*-algebraic twist for kernel machines.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Learning in rkhm: a c*-algebraic twist for kernel machines

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.820830Z

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Observation 5e637136-71d5-466c-8616-2a616c137640 · outbound

This paper cites Spectral Truncation Kernels: Noncommutativity in $C^*$-algebraic Kernel Machines.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Spectral Truncation Kernels: Noncommutativity in $C^*$-algebraic Kernel Machines

Reference 42

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Observation 02449fa7-18bb-4e52-a59c-d023ff10731d · outbound

This paper cites Supervised learning with quantum-enhanced feature spaces.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Supervised learning with quantum-enhanced feature spaces

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.806961Z

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Observation 967b293c-5309-4f07-8ef9-1913123a9660 · outbound

This paper cites Noisy quantum kernel machines.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Noisy quantum kernel machines

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.792881Z

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Observation 5fcea3c1-a3bc-4d97-ac03-65216b3eb015 · outbound

This paper cites Kernel methods in machine learning.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Kernel methods in machine learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.778810Z

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Observation 6aedad67-1f92-484c-8a6b-95488b93adf5 · outbound

This paper cites Preimage problem in kernel-based machine learning.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Preimage problem in kernel-based machine learning

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.765140Z

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Observation 66d1878c-65c9-4830-afe1-27dd68de620e · outbound

This paper cites an unresolved cited work.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Unresolved cited work

Reference 47

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Observation c730182f-b8e9-4db9-ab6d-d9cb47e08d37 · outbound

This paper cites Training quantum embedding kernels on near-term quantum computers.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Training quantum embedding kernels on near-term quantum computers

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.751698Z

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Observation ae9893a2-a95d-4243-8309-0a536a2389d9 · outbound

This paper cites Entangled kernels - beyond separability.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Entangled kernels - beyond separability

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.737992Z

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Observation f5e2bb38-28ec-47ca-9dfd-69671e4cf94d · outbound

This paper cites Multi-view metric learning in vector-valued kernel spaces.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Multi-view metric learning in vector-valued kernel spaces

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.724167Z

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Observation 8158761f-7143-4fe0-a4e6-141ec9c514a0 · outbound

This paper cites Automatic and effective discovery of quantum kernels.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Automatic and effective discovery of quantum kernels

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.709770Z

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

Observation c4752b59-8cca-44c3-84f1-7c15a43a5791 · outbound

This paper cites Seeking a quantum advantage for machine learning.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Seeking a quantum advantage for machine learning

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.695462Z

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

Observation b5a67acb-a69f-4f18-8d2b-f880c435bcb8 · outbound

This paper cites Quantum machine learning beyond kernel methods.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Quantum machine learning beyond kernel methods

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.681192Z

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 2f98cbd1-9645-45ad-a82b-1bc60d296f0f · outbound

This paper cites Machine learning: Trends, perspectives, and prospects.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Machine learning: Trends, perspectives, and prospects

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.666779Z

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 09e24857-05c8-4e6e-aff7-7a18448b82e2 · outbound

This paper cites On the role of entanglement in quantum-computational speed-up.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential On the role of entanglement in quantum-computational speed-up

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.652182Z

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 3d1a954d-c193-47fd-a700-2d57ebbe6551 · outbound

This paper cites A generalized kernel approach to structured output learning.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential A generalized kernel approach to structured output learning

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.636593Z

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 d59a0143-9e09-4d9c-8c79-fa5f3a75216f · outbound

This paper cites Operator-valued kernels for learning from functional response data.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Operator-valued kernels for learning from functional response data

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.622500Z

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 f7f04ac3-d36b-4e89-b2c1-bbb0f3dd47a5 · outbound

This paper cites Double descent in quantum machine learning.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Double descent in quantum machine learning

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T11:03:04.843537Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4f18ee1-ddc6-461e-8b96-fe1b4ec32826 · outbound

This paper cites u bler, Simon Buchholz, and Bernhard Sch \.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential u bler, Simon Buchholz, and Bernhard Sch \

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.607818Z

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 1692b746-1ba6-40c7-8533-98d7376586f7 · outbound

This paper cites Classically approximating variational quantum machine learning with random fourier features.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Classically approximating variational quantum machine learning with random fourier features

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.592827Z

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 a647a4d6-a10e-4371-82e8-206f77d49821 · outbound

This paper cites Theory of overparametrization in quantum neural networks.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Theory of overparametrization in quantum neural networks

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.576835Z

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 8c92dfdb-9fd8-4346-a0e1-fe25a5cb3698 · outbound

This paper cites Deep learning.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Deep learning

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T11:03:04.860085Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7019efb3-6bbe-4380-bf37-58b459f4b07a · outbound

This paper cites Operator-valued kernel-based vector autoregressive models for network inference.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Operator-valued kernel-based vector autoregressive models for network inference

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.550529Z

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 39c3dc48-a9e2-4ec8-aa3d-e0cb81d40d5f · outbound

This paper cites Random features for kernel approximation: A survey on algorithms, theory, and beyond.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Random features for kernel approximation: A survey on algorithms, theory, and beyond

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.534478Z

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 f9fa814f-8d0f-4049-b4e3-894126588c01 · outbound

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

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential A rigorous and robust quantum speed-up in supervised machine learning

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.518111Z

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 07cf3dc9-1883-4be2-b1ad-a53c83eabc13 · outbound

This paper cites Quantum embeddings for machine learning.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Quantum embeddings for machine learning

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T11:03:04.877002Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2223b697-3231-4485-b869-87c2b017be15 · outbound

This paper cites Kernel methods in quantum machine learning.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Kernel methods in quantum machine learning

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.503722Z

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 c3dcbc6c-599b-42df-a12f-25a420d90cf3 · outbound

This paper cites On learning vector-valued functions.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential On learning vector-valued functions

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.490232Z

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 243cc955-1500-489d-a0d4-c7b7cbd4577a · outbound

This paper cites Quantum Linear System Solvers: A Survey of Algorithms and Applications.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Quantum Linear System Solvers: A Survey of Algorithms and Applications

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-07T11:03:04.890204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ffa5e3f-ceb4-43ce-a48b-f2638a91fb84 · outbound

This paper cites Calibrating the role of entanglement in variational quantum circuits.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Calibrating the role of entanglement in variational quantum circuits

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.476129Z

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 817533d2-14e6-416c-986a-f3e9796fd890 · outbound

This paper cites Quantum computation and quantum information.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Quantum computation and quantum information

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-07T11:03:04.898768Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3784ef01-a35c-4797-b74a-305d17d63f14 · outbound

This paper cites Generalization despite overfitting in quantum machine learning models.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Generalization despite overfitting in quantum machine learning models

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.452299Z

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 8749a28c-d117-44d7-a15c-c9e576da331d · outbound

This paper cites Quantum computing in the NISQ era and beyond.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Quantum computing in the NISQ era and beyond

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-07T11:03:04.907090Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01b65faf-6d3c-4b1b-ba2e-61224807ff02 · outbound

This paper cites Neural quantum kernels: training quantum kernels with quantum neural networks.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Neural quantum kernels: training quantum kernels with quantum neural networks

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-07T11:03:04.911112Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6426502c-d437-4d84-98d3-ca3456a467e1 · outbound

This paper cites Quantum perceptron revisited: Computational-statistical tradeoffs.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Quantum perceptron revisited: Computational-statistical tradeoffs

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.427373Z

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 d836a390-5081-4afc-b63a-c70c864b0f9d · outbound

This paper cites Supervised quantum machine learning models are kernel methods.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Supervised quantum machine learning models are kernel methods

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-07T11:03:04.919706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b09ea2a8-0208-45da-957f-19b15b168e71 · outbound

This paper cites Quantum machine learning in feature hilbert spaces.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Quantum machine learning in feature hilbert spaces

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.413259Z

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 c8b451ce-9f25-44b0-af10-7894692b0db2 · outbound

This paper cites Is quantum advantage the right goal for quantum machine learning? Prx Quantum, 3 0 (3): 0 030101, 2022.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Is quantum advantage the right goal for quantum machine learning? Prx Quantum, 3 0 (3): 0 030101, 2022

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.398578Z

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 69fd3093-9888-483f-bd80-4fb6309e45b3 · outbound

This paper cites An introduction to quantum machine learning.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential An introduction to quantum machine learning

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.383326Z

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 3ea9983e-fcf4-4d97-9e42-981736859afc · outbound

This paper cites Schwartz.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Schwartz

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.368645Z

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 83964875-09cd-42d6-b59d-9c9fafe429f1 · outbound

This paper cites A graph to graphs framework for retrosynthesis prediction.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential A graph to graphs framework for retrosynthesis prediction

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.353875Z

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 9330f363-8b42-4c1a-b270-c28fb4a2ec00 · outbound

This paper cites Learning partial differential equations in reproducing kernel hilbert spaces.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Learning partial differential equations in reproducing kernel hilbert spaces

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.339405Z

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 e2d23ffc-6d58-4800-9010-40d6e7f36c56 · outbound

This paper cites Quantum Fisher kernel for mitigating the vanishing similarity issue.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Quantum Fisher kernel for mitigating the vanishing similarity issue

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-07T11:03:04.949109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa16af7d-3f3e-4e8e-bf96-ae5c9603b738 · outbound

This paper cites Graphqntk: Quantum neural tangent kernel for graph data.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Graphqntk: Quantum neural tangent kernel for graph data

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.323645Z

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 8b0e4d1b-9495-49c7-a371-e904557246f0 · outbound

This paper cites Quantum positional encodings for graph neural networks.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Quantum positional encodings for graph neural networks

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.308026Z

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 fd10153f-171a-44b0-8888-d6d3eb94fb7d · outbound

This paper cites Exponential concentration in quantum kernel methods.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Exponential concentration in quantum kernel methods

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.293573Z

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 203e69d0-1803-4785-b99f-bb9f3021c1eb · outbound

This paper cites Benign Overfitting with Quantum Kernels.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Benign Overfitting with Quantum Kernels

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-07T11:03:04.966354Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 779a12e8-543d-49b1-813f-f3ec84b3ac6f · outbound

This paper cites Large margin methods for structured and interdependent output variables.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Large margin methods for structured and interdependent output variables

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.279327Z

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 803d62fa-2d82-4c34-aca9-dd69e94a002d · outbound

This paper cites Towards understanding the power of quantum kernels in the NISQ era.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Towards understanding the power of quantum kernels in the NISQ era

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.264951Z

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 1b56c8f2-13ff-41dc-957a-2842dab72d0b · outbound

This paper cites The theory of quantum information.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential The theory of quantum information

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:05.249684Z

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 1f136991-cae2-4a6b-a749-8b8027ceb312 · outbound

This paper cites Link prediction based on graph neural networks.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Link prediction based on graph neural networks

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-07T11:03:04.982951Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers