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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 2026-08-18T06:34:40.430872+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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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T10:43:28.366506Z digest=sha256:160c8295b21f55eea7ef454ad4c884f7dd81875100663bab3e10bd3d465d119d

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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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T10:43:28.370752Z digest=sha256:5df8615816f0e2e82587bd7379bf92717dfb10093c07c910e7287d0b9857651b

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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source=pdf_text observed=2026-08-16T10:43:28.507759Z digest=sha256:1f4c3ee27dcfe3b97b7d789486a3276e9d054191834c44fd32d729d6e7478a81

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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T10:43:28.410015Z digest=sha256:8099fcdc11848a67d67b18f259b5db1b204411cfceb066260c4ed225af868790

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=sha256:68d004959f87145881909e9580f2934e8e0240893ecaad94c4d6e5d266466d7f

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=sha256:8df970a36d3cd836c444e93b46379d5442049335ca23ed45954f330c95f4b59d

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:f6a17f203642e553907b447631a878d0798a6a743fce115b3b2a13f92c906419

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:fcd731a49656c5c4c41141a28f31f75b535d2f5a930a3dbbc385fac6db03cc47

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

This paper cites an unresolved cited work.

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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source=pdf_text observed=2026-08-16T10:43:28.521738Z digest=sha256:e1e5b42b8cd2be1b3300bca3d65465b0ab14ecc6fabefabe72d52bd9563353c1

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=sha256:607ae424af338f1552d534d0bc0e74b3396d1e093b95a5adeb84a7d859a97aea

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=sha256:939428bef821a9c875e43811afa854aae95e5ed6970f47f43999cfb2edec5ce6

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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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=sha256:6073fd2239522e0a53cdc170e0ae5c3febe0b6a237f01f71e53f892b5053350f

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=sha256:8da1e8ec96d54137a523aab143a17724333915ff1d148afc2d5881c4e9793eb2

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:d69905ac016374222fc5a84e1eb2c3469ae8dcad16695326e9afa8572e3c79ce

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:f691729ef4a9bb674c7d23cf4e0c13f719a114edbc6ccdfb3821999416fe9ee9

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=sha256:8e3594ab0b57947d7f5d5c29129311993c2d37412bcfeac529575b80dc8474f2

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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T10:43:28.765598Z digest=sha256:ef8dc101c0c9768c52687956a31d8d5a29937c7679db1cbeadddd64d24ce28b6

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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raw_fallback, observed 2026-08-16T10:43:30.057259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T10:43:28.769842Z digest=sha256:0636f0caa472fa3dc0f02f63788aba292c1e94bc0a870bfe6c5273748dd08e56

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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no resolver link, observed 2026-08-16T10:43:28.772776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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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raw_fallback, observed 2026-08-16T10:43:29.899525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T10:43:28.776608Z digest=sha256:687b8a6fdd6001a6292871460bb4841b68dc52c93150a2130adf7e586f106fd9

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

Resolution
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Source-reported events for the cited work

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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

Resolution
unresolved
raw_fallback, observed 2026-08-16T10:43:29.797472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T10:43:28.787095Z digest=sha256:9e8491fc13687739fa0683695e39a8849ed6c4e2afb0fec491ffb1421ebd74b3

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

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

Source-reported events for the cited work

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
verified fuzzy
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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T10:43:28.794493Z digest=sha256:3594f54513fb2df65992066c5ad8b3a6ca7c5b97ffdf38e62fce457d1055644a

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T10:43:28.863615Z digest=sha256:e5cd1f41db1032db56a44ec1ad28a780567298306b8fd12dae66c54a74532de0

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=sha256:69a6de5786023ea07f67999e18d553c7630df746ed488ba1daa4e2f942361407

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T10:43:28.801725Z digest=sha256:40ec26ecf14fd5d02c4dce7ac050b2b466d4c771b99aeaf1628d0fa77fadc2f6

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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raw_fallback, observed 2026-08-16T10:43:29.775882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T10:43:28.808790Z digest=sha256:5512e2d92a2667040310be1a4c6520e4408f07c0f1416838a7868f637f2dbf02

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

Resolution
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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T10:43:28.812140Z digest=sha256:ea2f5f28514e84c7474afe87e734b3a24cc8af21b95c8edf37bb568cde49bb49

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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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T10:43:28.815071Z digest=sha256:a28393eee6b65e476ff5c6ec7e95282cf15c8e99658b331646328c19a3e2edbe

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=sha256:35f9f9eaa228fc62f9dc00105b288b70fb5ddca4a913a6d5ff7bf82aba94718a

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
verified fuzzy
raw_fallback, observed 2026-08-16T10:43:29.732348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T10:43:28.821860Z digest=sha256:1ef8ab4dde4903c8091cf544df0d43d1c8a281c1244baed7c3a060dbb22ab947

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
unresolved
raw_fallback, observed 2026-08-16T10:43:29.721023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T10:43:28.825274Z digest=sha256:ac90eda57fa45c2cfd9d16c3752f22353852e3005cd1fcea5643282f215570de

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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T10:43:28.828832Z digest=sha256:20bb0b6010cd0ae5404d4398d8f1e7468e4e696690ad171a8cc4043c8611b608

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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no resolver link, observed 2026-08-16T10:43:28.832112Z

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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

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

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T10:43:28.880049Z digest=sha256:5f7a512a58f38533cc7d97e156ca6252b419b374597c242dc7a9c39b029a6804

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

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T10:43:28.915100Z digest=sha256:b7bd94b01ad92dd3067ad47baea05371531abde039abf50f7d0f0569ce618f97

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:43:28.933701Z digest=sha256:cde923dc5dacbec7aae88012455ddc4aaf7844b336bd41021e8ce298b73664fe

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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T10:43:28.969431Z digest=sha256:7dd03682c21eb5db5acece858b35d44c6b5f89946a16bb5b9c26de97476a293d

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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unresolved
raw_fallback, observed 2026-08-16T10:43:29.372694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T10:43:29.016825Z digest=sha256:752846b7cbc894b175d6a0474a9cca17067b9287b49fca53dc62de499728e7f6

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:19:42.293611Z digest=sha256:d6abd7d6359e989d71f286bb03fa33918ee478b3a289278cfeeb360a115311be