The interplay of robustness and generalization in quantum machine learning | Paper Citation Record

Observation 2839797d-fe59-4e44-a42c-1c5b6e9bf236 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

The interplay of robustness and generalization in quantum machine learning Explaining and Harnessing Adversarial Examples

Reference 1

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This paper cites Intriguing properties of neural networks.

The interplay of robustness and generalization in quantum machine learning Intriguing properties of neural networks

Reference 2

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This paper cites Provable defenses against adversarial examples via the convex outer adversarial polytope,.

The interplay of robustness and generalization in quantum machine learning Provable defenses against adversarial examples via the convex outer adversarial polytope,

Reference 3

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Observation 77d2ce7c-a99b-4b57-b654-108e7c0355b4 · outbound

This paper cites Lipschitz-margin training: Scalable certification of perturbation invariance for deep neural networks,.

The interplay of robustness and generalization in quantum machine learning Lipschitz-margin training: Scalable certification of perturbation invariance for deep neural networks,

Reference 4

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Observation b1465fb7-9a34-44e2-96dd-b386c82c178f · outbound

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

The interplay of robustness and generalization in quantum machine learning Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 5

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This paper cites A simple weight decay can improve generalization,.

The interplay of robustness and generalization in quantum machine learning A simple weight decay can improve generalization,

Reference 6

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Observation 16c5c051-bc2b-4f30-95af-6fa943d4fe40 · outbound

This paper cites Robustness and generalization,.

The interplay of robustness and generalization in quantum machine learning Robustness and generalization,

Reference 7

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Observation 2f212eab-441c-48bf-aa6e-8f944eb6973b · outbound

This paper cites Distillation as a defense to adversarial perturbations against deep neural networks,.

The interplay of robustness and generalization in quantum machine learning Distillation as a defense to adversarial perturbations against deep neural networks,

Reference 8

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Observation 4c7a3be2-3992-47b1-845c-60db84a30b27 · outbound

This paper cites Quantum machine learning,.

The interplay of robustness and generalization in quantum machine learning Quantum machine learning,

Reference 9

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Observation 272d05d3-2d5f-49dd-b722-37e8dd007b10 · outbound

This paper cites Challenges and opportunities in quantum machine learning,.

The interplay of robustness and generalization in quantum machine learning Challenges and opportunities in quantum machine learning,

Reference 10

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Observation 3548969b-ad32-43fd-bdde-512ee8169ba2 · outbound

This paper cites Quantum adversarial machine learning: status, challenges and perspectives,.

The interplay of robustness and generalization in quantum machine learning Quantum adversarial machine learning: status, challenges and perspectives,

Reference 11

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Observation 8bf1c196-7c6c-49e4-990a-1bed021109e4 · outbound

This paper cites Vulnerability of quantum classification to adversarial perturbations,.

The interplay of robustness and generalization in quantum machine learning Vulnerability of quantum classification to adversarial perturbations,

Reference 12

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Observation 96e127dd-81ff-4090-b84a-06f121d0414b · outbound

This paper cites Quantum adversarial machine learning,.

The interplay of robustness and generalization in quantum machine learning Quantum adversarial machine learning,

Reference 13

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Observation a8cdea62-0c93-4ad3-af78-8b165e7aa15f · outbound

This paper cites Quantum noise protects quantum classifiers against adversaries,.

The interplay of robustness and generalization in quantum machine learning Quantum noise protects quantum classifiers against adversaries,

Reference 14

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Observation f8a6f3ee-2c86-4194-8324-2838c220bc74 · outbound

This paper cites Robustness verification of quantum classifiers,.

The interplay of robustness and generalization in quantum machine learning Robustness verification of quantum classifiers,

Reference 15

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This paper cites Robust in practice: adversarial attacks on quantum machine learning,.

The interplay of robustness and generalization in quantum machine learning Robust in practice: adversarial attacks on quantum machine learning,

Reference 16

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Observation 1b35a982-db8d-4969-80a8-5e2f6ba32026 · outbound

This paper cites Optimal provable robustness of quantum classification via quantum hypothesis testing,.

The interplay of robustness and generalization in quantum machine learning Optimal provable robustness of quantum classification via quantum hypothesis testing,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:43.349314Z

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Observation d4e41c5a-b07d-4259-a83e-187ce82e5612 · outbound

This paper cites Universal adversarial examples and perturbations for quantum classifiers,.

The interplay of robustness and generalization in quantum machine learning Universal adversarial examples and perturbations for quantum classifiers,

Reference 18

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Observation e75e12d7-fd3d-466a-9417-29419bc76614 · outbound

This paper cites Towards quantum enhanced adversarial robustness in machine learning,.

The interplay of robustness and generalization in quantum machine learning Towards quantum enhanced adversarial robustness in machine learning,

Reference 19

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This paper cites Bench- marking adversarially robust quantum machine learning at scale,.

The interplay of robustness and generalization in quantum machine learning Bench- marking adversarially robust quantum machine learning at scale,

Reference 20

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This paper cites Training robust and generalizable quantum models,.

The interplay of robustness and generalization in quantum machine learning Training robust and generalizable quantum models,

Reference 21

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Observation 849dd872-f128-48a6-894d-f8416ff2c30d · outbound

This paper cites The power of quantum neural networks,.

The interplay of robustness and generalization in quantum machine learning The power of quantum neural networks,

Reference 22

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Observation b9cd76f6-2ce6-4bbc-9b0c-0d2edb8360da · outbound

This paper cites Generalization in quantum machine learning: a quantum information standpoint,.

The interplay of robustness and generalization in quantum machine learning Generalization in quantum machine learning: a quantum information standpoint,

Reference 23

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Observation c1c088c9-a494-4159-be55-c057afa90df0 · outbound

This paper cites Encoding-dependent general- ization bounds for parametrized quantum circuits,.

The interplay of robustness and generalization in quantum machine learning Encoding-dependent general- ization bounds for parametrized quantum circuits,

Reference 24

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Observation 4ea447f3-4318-4984-9665-ae187d70576b · outbound

This paper cites Power of data in quantum machine learning,.

The interplay of robustness and generalization in quantum machine learning Power of data in quantum machine learning,

Reference 25

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Observation f72a5f26-fb4f-4c4f-93de-d01e6cc1077d · outbound

This paper cites Generalization in quantum machine learning from few training data,.

The interplay of robustness and generalization in quantum machine learning Generalization in quantum machine learning from few training data,

Reference 26

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This paper cites Out-of-distribution generalization for learning quantum dynamics,.

The interplay of robustness and generalization in quantum machine learning Out-of-distribution generalization for learning quantum dynamics,

Reference 27

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Observation d0a34851-0396-4604-9bbb-a43de4e37422 · outbound

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

The interplay of robustness and generalization in quantum machine learning Generalization despite overfitting in quantum machine learning models,

Reference 28

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Observation e0f33b8a-4d90-4664-a49b-22aa18a3347f · outbound

This paper cites Shadows of quantum machine learning,.

The interplay of robustness and generalization in quantum machine learning Shadows of quantum machine learning,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:43.130636Z

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Observation 1b9ddcc4-3a5f-442c-85bb-94eec1772965 · outbound

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

The interplay of robustness and generalization in quantum machine learning Understanding quantum machine learning also requires rethinking generalization,

Reference 30

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Observation bc0d77e8-2723-4498-b365-8d0d94914111 · outbound

This paper cites Parameterized quantum circuits as machine learning models,.

The interplay of robustness and generalization in quantum machine learning Parameterized quantum circuits as machine learning models,

Reference 31

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Observation e7d2ff4e-1415-4634-9f40-48c3459e1737 · outbound

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

The interplay of robustness and generalization in quantum machine learning Supervised learning with quantum-enhanced feature spaces,

Reference 32

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Observation 9a75a662-b22d-49e4-a832-f166bdc1da5c · outbound

This paper cites Quantum machine learning in feature Hilbert spaces,.

The interplay of robustness and generalization in quantum machine learning Quantum machine learning in feature Hilbert spaces,

Reference 33

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Observation 1b11bb84-4fcb-4708-8b78-b61bfab44244 · outbound

This paper cites Variational quantum algorithms,.

The interplay of robustness and generalization in quantum machine learning Variational quantum algorithms,

Reference 34

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Source-reported events for the cited work

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Observation a85194a4-ec08-49ba-bb95-5e41e04a29d0 · outbound

This paper cites Efficient and accurate estimation of Lipschitz constants for deep neural networks,.

The interplay of robustness and generalization in quantum machine learning Efficient and accurate estimation of Lipschitz constants for deep neural networks,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:43.020722Z

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Observation 4361f515-adc6-406d-afb4-0c7781555d8a · outbound

This paper cites A convex parameterization of robust recurrent neural networks,.

The interplay of robustness and generalization in quantum machine learning A convex parameterization of robust recurrent neural networks,

Reference 36

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Observation 4e566d08-77b6-4c7a-9173-356d07bc3312 · outbound

This paper cites Training robust neural networks using Lipschitz bounds,.

The interplay of robustness and generalization in quantum machine learning Training robust neural networks using Lipschitz bounds,

Reference 37

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Observation 46917cff-158b-4416-a1fa-d7df38a15cfb · outbound

This paper cites Data re-uploading for a universal quantum classifier,.

The interplay of robustness and generalization in quantum machine learning Data re-uploading for a universal quantum classifier,

Reference 38

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Source-reported events for the cited work

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Observation 8033a2e3-cd2e-4728-931a-93f143c11b93 · outbound

This paper cites Let quantum neural networks choose their own frequencies,.

The interplay of robustness and generalization in quantum machine learning Let quantum neural networks choose their own frequencies,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:42.956019Z

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Observation 865c2cb1-f2b9-4498-9b80-f44ab9aa169f · outbound

This paper cites Stochastic gradient descent for hybrid quantum-classical optimization,.

The interplay of robustness and generalization in quantum machine learning Stochastic gradient descent for hybrid quantum-classical optimization,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:42.937999Z

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Observation 4b4183f8-4567-4671-80d9-b6f36b905af2 · outbound

This paper cites Robustness of quantum algorithms against coherent control errors,.

The interplay of robustness and generalization in quantum machine learning Robustness of quantum algorithms against coherent control errors,

Reference 41

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Observation 75d3bf6f-4f94-4adf-ad2e-875b80f808e4 · outbound

This paper cites Single-shot quantum machine learning,.

The interplay of robustness and generalization in quantum machine learning Single-shot quantum machine learning,

Reference 42

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Observation fcb096e4-b397-4c9b-8690-78d862c64103 · outbound

This paper cites Double descent in quantum machine learning,.

The interplay of robustness and generalization in quantum machine learning Double descent in quantum machine learning,

Reference 43

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Source-reported events for the cited work

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Observation 20abe2b7-b961-4e52-8a0f-c8dccfcd3e78 · outbound

This paper cites Mohri, A.

The interplay of robustness and generalization in quantum machine learning Mohri, A

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:42.878008Z

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Observation cbec2f7f-4a76-4af8-a663-9ecc0ea6c138 · outbound

This paper cites Quantum circuit learning,.

The interplay of robustness and generalization in quantum machine learning Quantum circuit learning,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:42.859220Z

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Observation bb813d48-4c33-49d6-97df-1650eed5ca54 · outbound

This paper cites Circuit-centric quantum classifiers,.

The interplay of robustness and generalization in quantum machine learning Circuit-centric quantum classifiers,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:42.842099Z

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Observation 9b204caf-2521-480a-98ea-600784c92189 · outbound

This paper cites RobustnessGeneralizationQML,.

The interplay of robustness and generalization in quantum machine learning RobustnessGeneralizationQML,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:42.823222Z

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Observation 3c7a63be-1e03-4167-b1cb-02eae03a1a48 · outbound

This paper cites Simple mathematical models with very complicated dynamics,.

The interplay of robustness and generalization in quantum machine learning Simple mathematical models with very complicated dynamics,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:42.805680Z

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Observation ff056695-a1ab-4ddb-9b0f-699f0d00b3aa · outbound

This paper cites Devaney, An Introduction To Chaotic Dynamical Systems, 2nd ed.

The interplay of robustness and generalization in quantum machine learning Devaney, An Introduction To Chaotic Dynamical Systems, 2nd ed

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:42.783939Z

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Observation b4eeb1d9-1a44-4112-9dec-e300361a3bd5 · outbound

This paper cites Climate Predictions: The Chaos and Complexity in Climate Models,.

The interplay of robustness and generalization in quantum machine learning Climate Predictions: The Chaos and Complexity in Climate Models,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:42.760792Z

Source-reported events for the cited work

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Observation e2f08346-2492-4ccf-b96f-ee5020f17844 · outbound

This paper cites Chaos and nonlinear forecastability in economics and finance,.

The interplay of robustness and generalization in quantum machine learning Chaos and nonlinear forecastability in economics and finance,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:42.740408Z

Source-reported events for the cited work

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Observation 65af75f7-bf8c-4e5b-912d-7336ef630868 · outbound

This paper cites Quantum vs. classical: A comprehensive benchmark study for predicting time series with variational quantum machine learning,.

The interplay of robustness and generalization in quantum machine learning Quantum vs. classical: A comprehensive benchmark study for predicting time series with variational quantum machine learning,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:42.719278Z

Source-reported events for the cited work

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Observation b4964e5a-4fab-4f17-80a4-0f308c5e6364 · outbound

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

The interplay of robustness and generalization in quantum machine learning PennyLane: Automatic differentiation of hybrid quantum-classical computations

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T05:19:42.264100Z

Source-reported events for the cited work

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Observation 4b520857-02f6-411c-ba94-94f36f65ef27 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

The interplay of robustness and generalization in quantum machine learning Adam: A Method for Stochastic Optimization

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T05:19:42.270584Z

Source-reported events for the cited work

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Observation cde02294-894c-462f-97b3-7c68144b0cad · outbound

This paper cites A comparative analysis of adversarial robustness for quantum and classical machine learning,.

The interplay of robustness and generalization in quantum machine learning A comparative analysis of adversarial robustness for quantum and classical machine learning,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:42.700963Z

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Observation 8a61dff1-b95c-4949-bd17-4e3b739159d7 · outbound

This paper cites Robustness and Generalization in Quantum Reinforcement Learning via Lipschitz Regularization.

The interplay of robustness and generalization in quantum machine learning Robustness and Generalization in Quantum Reinforcement Learning via Lipschitz Regularization

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:19:42.381786Z

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Observation 60927754-317e-4160-b44d-4fbcac16b331 · outbound

This paper cites Adversarial quantum machine learning: an information-theoretic generalization analysis,.

The interplay of robustness and generalization in quantum machine learning Adversarial quantum machine learning: an information-theoretic generalization analysis,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:42.682090Z

Source-reported events for the cited work

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

This paper cites On the Generalization of Adversarially Trained Quantum Classifiers.

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

Reference 58

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

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