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Paper Citation Record · LEDGER

On the Generalization of Adversarially Trained Quantum Classifiers

As of 18 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 1 inbound Pith citation observation for arXiv:2504.17690.

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

pith.paper-citation-record.v1
2504.17690 v1

Coverage vector

measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:43:29.016825Z

measured 62 of 62 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:19:42.293611Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T05:19:42.342253Z

Reference resolution

61 of 61 outbound references displayed

  • verified exact2
  • verified fuzzy20
  • unresolved37
  • parse uncertain0
  • malformed identifier2
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 07e8d2aa-73fb-43de-92e5-445365fb7545 · outbound

This paper cites To this end, the following proposition upper bounds the scaled excess RCSC r,p,ϵ for the quantum embeddings introduced in Section II A.

On the Generalization of Adversarially Trained Quantum Classifiers To this end, the following proposition upper bounds the scaled excess RCSC r,p,ϵ for the quantum embeddings introduced in Section II A

Reference 1

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source=pdf_text observed=2026-08-16T10:43:28.366506Z digest=sha256:c4c31e06e8d1304139b00e1b06c962b23f26041a9bc24e1b4360c2597863e9b5

Observation 53081555-ea74-423a-98fc-e74f61341a9a · outbound

This paper cites sup f∈Fr mX i=1 σi min x′:∥xi−x′∥p≤ϵ yif(x′) +yif(xi)−yif(xi) # (a) ≤ mR(Fr) + Eσ.

On the Generalization of Adversarially Trained Quantum Classifiers sup f∈Fr mX i=1 σi min x′:∥xi−x′∥p≤ϵ yif(x′) +yif(xi)−yif(xi) # (a) ≤ mR(Fr) + Eσ

Reference 2

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source=pdf_text observed=2026-08-16T10:43:28.370752Z digest=sha256:4ea2d7c6c2f82fe7279ae8627a20ab28460c9a3e6211a44186c0dc366ca82d41

Observation a4edb058-bc8e-4f17-8550-c10f52a1d372 · outbound

This paper cites an unresolved cited work.

On the Generalization of Adversarially Trained Quantum Classifiers Unresolved cited work

Reference 3

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Observation 12ec13a0-e15e-4d2a-902b-400c1e335574 · outbound

This paper cites an unresolved cited work.

On the Generalization of Adversarially Trained Quantum Classifiers Unresolved cited work

Reference 4

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:43:28.536595Z digest=sha256:d018af213fec1d3383e482cc076aa783e6673125044c0bebfa7423e5b867dfff

Observation 56536ff0-e3eb-4152-878d-3ca92f43119c · outbound

This paper cites an unresolved cited work.

On the Generalization of Adversarially Trained Quantum Classifiers Unresolved cited work

Reference 5

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:43:28.410015Z digest=sha256:6293292201a00aa5dd6ad2217dbccfef1b6f66b3044db452db5bd1ecebf3f81a

Observation 43875c21-cade-43d1-bf58-dec256a34b1a · outbound

This paper cites A direct computation yields that | ψ(x +δx)|ψ(x)⟩ 2 = dX i,j=1 xi(xi +δxi)xj(xj +δxj) ∥x∥2 2∥x +δx∥2 2 where we write x′ = x +δx.

On the Generalization of Adversarially Trained Quantum Classifiers A direct computation yields that | ψ(x +δx)|ψ(x)⟩ 2 = dX i,j=1 xi(xi +δxi)xj(xj +δxj) ∥x∥2 2∥x +δx∥2 2 where we write x′ = x +δx

Reference 6

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source=pdf_text observed=2026-08-16T10:43:28.467523Z digest=sha256:400b9b753e2d1e88ccf9bb9c537ec047a20a3017133252bc29f864d087449613

Observation 7b75bd3f-4677-4b46-b264-d14c8d820b7f · outbound

This paper cites Assume the solution is∥ρ(x)−ρ(x′)∥1≤ L∥U(x)−U (x′)∥⋄ Proof.

On the Generalization of Adversarially Trained Quantum Classifiers Assume the solution is∥ρ(x)−ρ(x′)∥1≤ L∥U(x)−U (x′)∥⋄ Proof

Reference 7

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source=pdf_text observed=2026-08-16T10:43:28.498941Z digest=sha256:79d4db52271c8846118973ff6485fa1a51608b361672fa3e80f720f1ed9dabd5

Observation a3b5d650-bf7e-4f6e-a3d3-65cad2124298 · outbound

This paper cites an unresolved cited work.

On the Generalization of Adversarially Trained Quantum Classifiers Unresolved cited work

Reference 8

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source=pdf_text observed=2026-08-16T10:43:28.553387Z digest=sha256:a7ebb91ea2ab89085e65b6268549bb274d285f2514589de71c7d98d051d17058

Observation d51a8467-4ffc-4add-bc05-f8e63a99b2bc · outbound

This paper cites mX i=1 σiyiTr Aρ(xi) # = 1 m E σ.

On the Generalization of Adversarially Trained Quantum Classifiers mX i=1 σiyiTr Aρ(xi) # = 1 m E σ

Reference 9

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source=pdf_text observed=2026-08-16T10:43:28.511199Z digest=sha256:c2e648edb04e0b818c9c1a29a1fe1a6a850bda8d70513d094eebca7860666b73

Observation 29135c8a-e844-4536-a083-f02e99cf87a9 · outbound

This paper cites an unresolved cited work.

On the Generalization of Adversarially Trained Quantum Classifiers Unresolved cited work

Reference 10

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source=pdf_text observed=2026-08-16T10:43:28.514503Z digest=sha256:1ec5cabf6d42fd16ae67a9cef9aad467b49b70ffa841090df180e228d1ecf625

Observation b52b5b72-f1ef-4bbb-b3d6-7f10cebb95ac · outbound

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On the Generalization of Adversarially Trained Quantum Classifiers Unresolved cited work

Reference 11

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Observation 6d47c833-4049-4b79-9d84-b0c28fc8f2e0 · outbound

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On the Generalization of Adversarially Trained Quantum Classifiers Unresolved cited work

Reference 12

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Observation ad43e783-4d9b-4da4-ad75-631927f1fe68 · outbound

This paper cites an unresolved cited work.

On the Generalization of Adversarially Trained Quantum Classifiers Unresolved cited work

Reference 13

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Observation c9696d0e-af2a-4988-8be6-39b0e4f469d4 · outbound

This paper cites Rist` e, M.

On the Generalization of Adversarially Trained Quantum Classifiers Rist` e, M

Reference 14

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source=pdf_text observed=2026-08-16T10:43:28.529289Z digest=sha256:cae11fdb5b444800488d1866d50484e26e5b9695d80c1c9fcd547f2ff5518d94

Observation ab8563f3-b189-4b21-8f25-8deb0cbfe79c · outbound

This paper cites an unresolved cited work.

On the Generalization of Adversarially Trained Quantum Classifiers Unresolved cited work

Reference 15

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source=pdf_text observed=2026-08-16T10:43:28.532677Z digest=sha256:a3b1a2892b6a1489d62bef6717f306a01de01bdba31e1a3aea698f5fe59d63b2

Observation b4e4cd3a-df72-4a0b-b1f4-190f32b7be2d · outbound

This paper cites QFAL: Quantum Federated Adversarial Learning.

On the Generalization of Adversarially Trained Quantum Classifiers QFAL: Quantum Federated Adversarial Learning

Reference 16

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source=pdf_text observed=2026-08-16T10:43:28.597097Z digest=sha256:9906c020e269f8a0054e88db6c6edf08238ca6eaee8b5c00db041fd5903581ce

Observation 6179b791-690b-477b-aa91-2d7a1e645459 · outbound

This paper cites Quantum Convolutional Neural Networks are Effectively Classically Simulable.

On the Generalization of Adversarially Trained Quantum Classifiers Quantum Convolutional Neural Networks are Effectively Classically Simulable

Reference 17

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source=pdf_text observed=2026-08-16T10:43:28.540386Z digest=sha256:e210651cc6238c7df4103c8dd7aec3f03d4dbe2345778b16078c71a7bb628b45

Observation aa2ee49b-cc27-46ef-abbd-6e278332abcc · outbound

This paper cites Lu, L.-M.

On the Generalization of Adversarially Trained Quantum Classifiers Lu, L.-M

Reference 18

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source=pdf_text observed=2026-08-16T10:43:28.545102Z digest=sha256:0fcaa621f36cf6fb6e90dad2c8fe037d1a960589b35674ebea61c46a58b6f9e7

Observation df5eb424-3bc5-4f89-bbd8-6650806ee00a · outbound

This paper cites an unresolved cited work.

On the Generalization of Adversarially Trained Quantum Classifiers Unresolved cited work

Reference 19

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Observation 35c980c1-0550-4810-b3e4-f7ba9818a21b · outbound

This paper cites Banchi, J.

On the Generalization of Adversarially Trained Quantum Classifiers Banchi, J

Reference 20

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source=pdf_text observed=2026-08-16T10:43:28.631667Z digest=sha256:c5fe70d2de9ecfdf5be1cf85046e0b18c27152ac9423ade4352e7fed6b4e4880

Observation 664befff-a8db-4ede-8bf7-aa49fd8260e3 · outbound

This paper cites Du, M.-H.

On the Generalization of Adversarially Trained Quantum Classifiers Du, M.-H

Reference 21

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source=pdf_text observed=2026-08-16T10:43:28.557585Z digest=sha256:9d73c74455582fbb78e0abcb8ee45a7014346b791a8adce2adfeab1bfc82c3b4

Observation d7d41551-c844-48a1-aec3-f04ff631dc8e · outbound

This paper cites an unresolved cited work.

On the Generalization of Adversarially Trained Quantum Classifiers Unresolved cited work

Reference 22

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source=pdf_text observed=2026-08-16T10:43:28.573416Z digest=sha256:e8b2cf69d957b686dd960e63a55d76588877a2b92438edc64a30b243c3a50c5a

Observation e28fa0c7-55fc-4496-9f05-5ffb9f5366e6 · outbound

This paper cites Schmidt, S.

On the Generalization of Adversarially Trained Quantum Classifiers Schmidt, S

Reference 23

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source=pdf_text observed=2026-08-16T10:43:28.577377Z digest=sha256:691be68b3fb900c5489c76aadf4afb43e2db8de9e98e4a1976756b35aeff6026

Observation f27d82f5-4782-472d-b898-926870f63cc8 · outbound

This paper cites sup f∈Fr 1 m mX i=1 σiϕ(yif(xi)) # . Sinceϕ(·) isη-Lipschitz,R(ℓ◦Gr) can be upper bounded using Talagrand’s contraction inequality [45] as R(ℓ◦G )≤ηR(Fr) with R(Fr) = Eσ.

On the Generalization of Adversarially Trained Quantum Classifiers sup f∈Fr 1 m mX i=1 σiϕ(yif(xi)) # . Sinceϕ(·) isη-Lipschitz,R(ℓ◦Gr) can be upper bounded using Talagrand’s contraction inequality [45] as R(ℓ◦G )≤ηR(Fr) with R(Fr) = Eσ

Reference 24

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source=pdf_text observed=2026-08-16T10:43:28.357962Z digest=sha256:a044c2170f8e6f633744c27287c6495eff736ce65d9eb9b1e6f26b6c1097278d

Observation dfd9c35d-db91-4ac0-a095-76302e1cc21a · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

On the Generalization of Adversarially Trained Quantum Classifiers Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 25

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source=pdf_text observed=2026-08-16T10:43:28.581298Z digest=sha256:91a081bcb659ea39cee2889c19ea9666c3572a8c4da242a3793af47bb5184697

Observation 8dfd1398-9638-4e2c-8ee8-3d21bd9a1e93 · outbound

This paper cites an unresolved cited work.

On the Generalization of Adversarially Trained Quantum Classifiers Unresolved cited work

Reference 26

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source=pdf_text observed=2026-08-16T10:43:28.585213Z digest=sha256:203e3f9684aa71278ccf7c5e157c0b9d0137312435bf0fce94b3b598564ccbd3

Observation 29a83de9-1d8e-484d-b2cc-9d380a820a09 · outbound

This paper cites Adversarial Rademacher Complexity of Deep Neural Networks.

On the Generalization of Adversarially Trained Quantum Classifiers Adversarial Rademacher Complexity of Deep Neural Networks

Reference 27

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source=pdf_text observed=2026-08-16T10:43:28.589017Z digest=sha256:19547e1478130a16d75f36a65f4327f15c8a5f5813d6801bb7c7253e93042d76

Observation 03a107a7-8509-4630-99d7-a4e4a4af2053 · outbound

This paper cites Quantum Adversarial Learning for Kernel Methods.

On the Generalization of Adversarially Trained Quantum Classifiers Quantum Adversarial Learning for Kernel Methods

Reference 28

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local_arxiv, observed 2026-08-16T10:43:29.325194Z

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source=pdf_text observed=2026-08-16T10:43:28.593349Z digest=sha256:fe380abb43064ecbf08f07594cbf49de4b2060c40420f998cb306d855fde25cd

Observation d4e2716c-54d2-43b8-85eb-a5fe164bd006 · outbound

This paper cites Schuld and F.

On the Generalization of Adversarially Trained Quantum Classifiers Schuld and F

Reference 29

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source=pdf_text observed=2026-08-16T10:43:28.601498Z digest=sha256:3c91d25c6a9eb49d16499a2892c1198861f894f105f413b80a3f62f087822206

Observation d2e57ad9-1cae-49bd-bcae-0e3a4cfe43c8 · outbound

This paper cites Simeone et al., An introduction to quantum machine learning for engineers, Foundations and Trends® in Sig- nal Processing 16, 1 (2022).

On the Generalization of Adversarially Trained Quantum Classifiers Simeone et al., An introduction to quantum machine learning for engineers, Foundations and Trends® in Sig- nal Processing 16, 1 (2022)

Reference 30

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source=pdf_text observed=2026-08-16T10:43:28.604758Z digest=sha256:0db80a0057e6b46db6a0be30d41571d7510b989ebd16c3c2073b114d3170c525

Observation bc491462-aadf-4cc3-b6d6-45bd334f5dd1 · outbound

This paper cites an unresolved cited work.

On the Generalization of Adversarially Trained Quantum Classifiers Unresolved cited work

Reference 31

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source=pdf_text observed=2026-08-16T10:43:28.608453Z digest=sha256:deffed5659ba71ebd7aad71f299ca4799bef8e19b40825622e1655770a988bb1

Observation c6b9e2c9-9506-4851-a09c-93cfb2eb8468 · outbound

This paper cites Stability and Generalization of Quantum Neural Networks.

On the Generalization of Adversarially Trained Quantum Classifiers Stability and Generalization of Quantum Neural Networks

Reference 32

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source=pdf_text observed=2026-08-16T10:43:28.798107Z digest=sha256:f3ad86b99735801864109b76e9e9506043a1d9db0c78cbb74a888b73f07c7b53

Observation fe884c70-a884-4597-81dd-25e6bb331a9d · outbound

This paper cites Dowling, M.

On the Generalization of Adversarially Trained Quantum Classifiers Dowling, M

Reference 33

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source=pdf_text observed=2026-08-16T10:43:28.710466Z digest=sha256:64b939d595d86235a0646a39e4a1a068b630b70de786aedd086669842f35b72a

Observation 31670d03-7ab5-4113-95b1-5026f5933a40 · outbound

This paper cites LaRose and B.

On the Generalization of Adversarially Trained Quantum Classifiers LaRose and B

Reference 34

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source=pdf_text observed=2026-08-16T10:43:28.760887Z digest=sha256:db1a12cdbe717a75fe46b9c5aae4874cbe507b1d8afb7b710764ed78d97425c0

Observation 3961fb69-e00d-47fd-9b3b-978d802c2dce · outbound

This paper cites Mohri, A.

On the Generalization of Adversarially Trained Quantum Classifiers Mohri, A

Reference 35

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:43:28.765598Z digest=sha256:629fd8dfbbab9999f21638f3ee5703fb35585c09a64221c91b81f2a2ca41d31e

Observation f94401cd-a7d2-47f4-a21c-d0665e5bf37c · outbound

This paper cites Georgiou, S.

On the Generalization of Adversarially Trained Quantum Classifiers Georgiou, S

Reference 36

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:43:28.769842Z digest=sha256:42ff35bce7833fe6102dabf8c53dff67dbed800e762ceca38fd234e7a448cdb3

Observation 81f22075-d376-4a62-8390-f47feadef002 · outbound

This paper cites Adversarial Risk Bounds via Function Transformation.

On the Generalization of Adversarially Trained Quantum Classifiers Adversarial Risk Bounds via Function Transformation

Reference 37

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-16T10:43:28.772776Z digest=sha256:37c5a52bfa9a03b2a13fc03ef72921611f9a9db366565b1d6e9425c2a5ed0126

Observation f5ebb3f1-6d61-4c72-9752-8982fea34008 · outbound

This paper cites Awasthi, N.

On the Generalization of Adversarially Trained Quantum Classifiers Awasthi, N

Reference 38

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:43:28.776608Z digest=sha256:0a9b9bbef1f1c32757795ae9edb81d5fa9947f95a3f52c4fc7c97ef0d41e0c4f

Observation 5130144c-d7cd-490f-a374-20eccb67defb · outbound

This paper cites Vershynin, High-dimensional probability: An introduc- tion with applications in data science, Vol.

On the Generalization of Adversarially Trained Quantum Classifiers Vershynin, High-dimensional probability: An introduc- tion with applications in data science, Vol

Reference 39

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source=pdf_text observed=2026-08-16T10:43:28.780559Z digest=sha256:1bd0404c315160fe9f8233ecfcf546a369eee44c24a45f23b71fc29b871cbb7b

Observation 973af591-1929-43bb-b716-1eafaa0bf865 · outbound

This paper cites an unresolved cited work.

On the Generalization of Adversarially Trained Quantum Classifiers Unresolved cited work

Reference 40

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source=pdf_text observed=2026-08-16T10:43:28.783536Z digest=sha256:3e21ad5ae1d819a1b1b2a27174d9a68b872948f4c1bb95d3addca5817c310051

Observation cf650c06-2c5d-460b-be26-26f25eac9d66 · outbound

This paper cites an unresolved cited work.

On the Generalization of Adversarially Trained Quantum Classifiers Unresolved cited work

Reference 41

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unresolved
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:43:28.787095Z digest=sha256:da79bebaaa3018daa70c2d4109a46ef0e5a840bad66240883ca098fb1b3bba9f

Observation 6685f5ef-6d68-4a23-a267-f4c83ff94d8c · outbound

This paper cites Statistical Complexity of Quantum Learning.

On the Generalization of Adversarially Trained Quantum Classifiers Statistical Complexity of Quantum Learning

Reference 42

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unresolved
no resolver link, observed 2026-08-16T10:43:28.790465Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:43:28.790465Z digest=sha256:769572e5e3e5638023340a75d9caa9338f301260bf0a789762f18d28fc7aa1b5

Observation 57e29a2a-0759-4f09-8236-44528307fcbf · outbound

This paper cites Theresa Jose and O.

On the Generalization of Adversarially Trained Quantum Classifiers Theresa Jose and O

Reference 43

Resolution
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raw_fallback, observed 2026-08-16T10:43:29.786288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:43:28.794493Z digest=sha256:0134c6310c4c2eadf536a7fe8290528c519d0d3ded54682e1a467ab1ce4c8ba7

Observation 3ee27421-011f-49c5-8664-462090a83640 · outbound

This paper cites Shalev-Shwartz and S.

On the Generalization of Adversarially Trained Quantum Classifiers Shalev-Shwartz and S

Reference 44

Resolution
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raw_fallback, observed 2026-08-16T10:43:29.453284Z

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source=pdf_text observed=2026-08-16T10:43:28.863615Z digest=sha256:10787700d5f5359824116eb032ff1cbf2eb9e538283c6b4385e844a51ab3f425

Observation 5fcd4957-506d-4695-82a1-430bd270e5ef · outbound

This paper cites Similarly, under quantum attack, we get that R(ℓQ r,p,ϵ◦ Gr)≤ηR(FQ r,p,ϵ) with FQ r,p,ϵ = n (x,y )7→h(x,y ) = min ρ′:∥ρ(x)−ρ′∥p≤ϵ yTr(Aρ′) :A∈A r o.

On the Generalization of Adversarially Trained Quantum Classifiers Similarly, under quantum attack, we get that R(ℓQ r,p,ϵ◦ Gr)≤ηR(FQ r,p,ϵ) with FQ r,p,ϵ = n (x,y )7→h(x,y ) = min ρ′:∥ρ(x)−ρ′∥p≤ϵ yTr(Aρ′) :A∈A r o

Reference 45

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source=pdf_text observed=2026-08-16T10:43:28.362823Z digest=sha256:40fc82e118ad8235068bdaa7983b0fcd8713c735eaf181686ef8839e2fc9a766

Observation 622d2f87-68c0-4be8-aeeb-c73ca30cac80 · outbound

This paper cites Optimizer-Dependent Generalization Bound for Quantum Neural Networks.

On the Generalization of Adversarially Trained Quantum Classifiers Optimizer-Dependent Generalization Bound for Quantum Neural Networks

Reference 46

Resolution
verified exact
local_arxiv, observed 2026-08-16T10:43:29.204548Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:43:28.801725Z digest=sha256:5e769c63fc917c1e59c8476dafb979b813c0f90bad9043b5a47c6faa7386cd96

Observation 990368a1-8591-4676-b212-3b6fb90378f2 · outbound

This paper cites Berberich, D.

On the Generalization of Adversarially Trained Quantum Classifiers Berberich, D

Reference 47

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source=pdf_text observed=2026-08-16T10:43:28.805381Z digest=sha256:716b49cb706e91b80244609c53940849a6ba0317f9a2b13984417e1b4ad27b81

Observation b96fe6c6-9d2c-41f5-9240-1f694e3aae49 · outbound

This paper cites Huang and S.

On the Generalization of Adversarially Trained Quantum Classifiers Huang and S

Reference 48

Resolution
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source=pdf_text observed=2026-08-16T10:43:28.808790Z digest=sha256:73bfa53274a318cf78c12921d43be2133a7b22ec1c5a5a44e82dc34b3b46ec31

Observation f7ce963c-22db-470f-afbd-4999cf728ea7 · outbound

This paper cites an unresolved cited work.

On the Generalization of Adversarially Trained Quantum Classifiers Unresolved cited work

Reference 49

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source=pdf_text observed=2026-08-16T10:43:28.812140Z digest=sha256:992804b8444e520955ba7bda3832666b4997c4674958359cd0426469f263ff14

Observation 322054e4-25cd-4724-86fe-3ef3972feb9c · outbound

This paper cites an unresolved cited work.

On the Generalization of Adversarially Trained Quantum Classifiers Unresolved cited work

Reference 50

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source=pdf_text observed=2026-08-16T10:43:28.815071Z digest=sha256:6f0e523baf08ac31fa807100a831ef040f1619ae075139c443d189501062e151

Observation b8c0b52a-9afc-4699-8f33-ff98f235e478 · outbound

This paper cites Huang, Y.-L.

On the Generalization of Adversarially Trained Quantum Classifiers Huang, Y.-L

Reference 51

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source=pdf_text observed=2026-08-16T10:43:28.818878Z digest=sha256:3f624d87e89d7c7b3afe80840ac8b38e2e022a866e789f239eef223072518a91

Observation cb0dee4a-0429-4aed-a6ff-d34c248f4bc7 · outbound

This paper cites Liu and P.

On the Generalization of Adversarially Trained Quantum Classifiers Liu and P

Reference 52

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:43:28.821860Z digest=sha256:a4362e3337cd8ddc4fccacdb40296d92666bdfd1d473ebfb8530ce7a43f889e8

Observation 7e30c0fe-03a2-4dfd-8b18-b1f968d07d99 · outbound

This paper cites an unresolved cited work.

On the Generalization of Adversarially Trained Quantum Classifiers Unresolved cited work

Reference 53

Resolution
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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:43:28.825274Z digest=sha256:61c920fac01cec797a73fdad0888cc40e91f5d80206d523bacae0eefff815b2e

Observation 1af4df72-42c6-4342-aa06-b352bd5987f3 · outbound

This paper cites Gyurik, V.

On the Generalization of Adversarially Trained Quantum Classifiers Gyurik, V

Reference 54

Resolution
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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:43:28.828832Z digest=sha256:038086403775ffc615808af1ba8db079d16f1473fda493f797eb6ee31e1fbf17

Observation 1a5c87df-263c-4fe6-adeb-2a6be1ac84eb · outbound

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

On the Generalization of Adversarially Trained Quantum Classifiers Supervised quantum machine learning models are kernel methods

Reference 55

Resolution
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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:43:28.832112Z digest=sha256:18b920bf4163707da6e76a79fa08c2bb5546181834a1ddd003b3ecdd05892404

Observation 26aaa362-0fb2-43f7-8d0f-959138de990e · outbound

This paper cites Schuld, R.

On the Generalization of Adversarially Trained Quantum Classifiers Schuld, R

Reference 56

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source=pdf_text observed=2026-08-16T10:43:28.851986Z digest=sha256:5e8d7e02863e8508ddbde00aed3b9ec74ad71d531069847fad26820b1aed67ec

Observation 5c680724-6fd7-4596-a58c-1677ef56c299 · outbound

This paper cites an unresolved cited work.

On the Generalization of Adversarially Trained Quantum Classifiers Unresolved cited work

Reference 57

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raw_fallback, observed 2026-08-16T10:43:29.406634Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:43:28.880049Z digest=sha256:d13a19ff5d0dc77f1b99afa3afccc90183b7e78c9525f78d752e2815b36108ce

Observation e7c7a7d7-6797-4e51-a14e-c372f9231577 · outbound

This paper cites an unresolved cited work.

On the Generalization of Adversarially Trained Quantum Classifiers Unresolved cited work

Reference 58

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unresolved
raw_fallback, observed 2026-08-16T10:43:29.394775Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:43:28.915100Z digest=sha256:33f333b387549c9616c512257bd2b1f81cc717d93f6c2dad6be00baf0a109ee5

Observation 9b509161-55ae-46a1-9243-501d15f41072 · outbound

This paper cites PennyLane: Automatic differentiation of hybrid quantum-classical computations.

On the Generalization of Adversarially Trained Quantum Classifiers PennyLane: Automatic differentiation of hybrid quantum-classical computations

Reference 59

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no resolver link, observed 2026-08-16T10:43:28.933701Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:43:28.933701Z digest=sha256:ca2906a8668777a417d2bf3eee1429d2a7fe1c0d364fb0535526d0bc139e439a

Observation afc3bc68-4295-4312-875f-47b2b80c0fb2 · outbound

This paper cites an unresolved cited work.

On the Generalization of Adversarially Trained Quantum Classifiers Unresolved cited work

Reference 60

Resolution
unresolved
raw_fallback, observed 2026-08-16T10:43:29.383735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:43:28.969431Z digest=sha256:a39fc937b211e29a821bdfc2c122efd4cac535ca9e9b66e76df8422581d5b284

Observation 777e303c-5ddb-404f-9e0f-5557f9889175 · outbound

This paper cites an unresolved cited work.

On the Generalization of Adversarially Trained Quantum Classifiers Unresolved cited work

Reference 61

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:43:29.016825Z digest=sha256:7c751759938e734402986072da92678b15ad543243ece1c21009a30b56df0766

Pith citing papers

Observation 075ce0bc-cc8a-4803-ae5e-33da71a19b7a · inbound

The interplay of robustness and generalization in quantum machine learning cites this paper.

The interplay of robustness and generalization in quantum machine learning On the Generalization of Adversarially Trained Quantum Classifiers

Reference 58

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verified exact
local_arxiv, observed 2026-08-07T05:19:42.351696Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:19:42.293611Z digest=sha256:6a74c7e448955cb51cc567bd683815384075d1b245e5275ddf2c724c1748efde