Paper Citation Record · LEDGER

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise

As of 15 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2505.18478.

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

pith.paper-citation-record.v1
2505.18478 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

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One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

47 of 47 outbound references displayed

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External citation measurements

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Outbound references

Observation c92a1d7e-c161-48d4-a947-b6b9f6c2aad8 · outbound

This paper cites URL https:// github.com/qiskit-community/ qiskit-machine-learning/blob/stable/0.7/ docs/tutorials/11_quantum_convolutional_ neural_networks.ipynb.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise URL https:// github.com/qiskit-community/ qiskit-machine-learning/blob/stable/0.7/ docs/tutorials/11_quantum_convolutional_ neural_networks.ipynb

Reference 1

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Observation 6f05bf38-98e4-43ef-b7e2-fc5a97243011 · outbound

This paper cites On quantum backpropagation, information reuse, and cheating measurement collapse.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise On quantum backpropagation, information reuse, and cheating measurement collapse

Reference 2

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This paper cites Natural evolutionary strategies for variational quantum computation.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Natural evolutionary strategies for variational quantum computation

Reference 3

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Observation 7f863319-aa77-4a94-81d6-d95bb8f3f328 · outbound

This paper cites Quantum machine learning.Nature, 549 (7671):195–202, September 2017.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Quantum machine learning.Nature, 549 (7671):195–202, September 2017

Reference 4

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This paper cites Wang, Sepehr Ebadi, Marcin Kali- nowski, Alexander Keesling, Nishad Maskara, Hannes Pichler, Markus Greiner, Vladan Vuletić, and Mikhail D.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Wang, Sepehr Ebadi, Marcin Kali- nowski, Alexander Keesling, Nishad Maskara, Hannes Pichler, Markus Greiner, Vladan Vuletić, and Mikhail D

Reference 5

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Observation 1b206ab8-6755-45e8-a5c1-7929eb05551b · outbound

This paper cites Evered, Alexan- dra A.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Evered, Alexan- dra A

Reference 6

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Observation bd826410-209f-4964-b663-f7f9489f0e54 · outbound

This paper cites Melko, and Simon Trebst.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Melko, and Simon Trebst

Reference 7

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Observation 60145bc4-1bca-4d8e-b252-4c97d6861ca2 · outbound

This paper cites Quantum Error Mitigation.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Quantum Error Mitigation

Reference 8

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This paper cites Publisher: Nature Pub- lishing Group.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Publisher: Nature Pub- lishing Group

Reference 9

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Observation 5228e607-ced4-44be-a753-554907f18546 · outbound

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Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Unresolved cited work

Reference 10

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Observation 6abbae85-db14-4821-8889-7c3ffa897a29 · outbound

This paper cites Variational Quantum Algorithms.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Variational Quantum Algorithms

Reference 11

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Observation 0c023fc6-ee7d-4c1b-a279-70c77219ef13 · outbound

This paper cites Cohen, Elan Rosenfeld, and J.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Cohen, Elan Rosenfeld, and J

Reference 12

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Observation 70d8d9af-83ea-4a51-8d9c-8ccfc4c3f910 · outbound

This paper cites Caro, Hsin-Yuan Huang, M.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Caro, Hsin-Yuan Huang, M

Reference 13

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This paper cites DOI: 10.1038/s41467- 022-32550-3.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise DOI: 10.1038/s41467- 022-32550-3

Reference 14

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Observation 078b2d3b-d038-4ed8-ae3f-abe002b5542e · outbound

This paper cites A Quantum Approximate Optimization Algorithm.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise A Quantum Approximate Optimization Algorithm

Reference 15

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

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Understanding quantum machine learning also requires rethinking generalization

Reference 16

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Observation 9db3481c-fd4b-4474-a868-7a532390f67a · outbound

This paper cites The CMA Evolution Strategy: A Comparing Review.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise The CMA Evolution Strategy: A Comparing Review

Reference 17

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Observation 56f86acb-8d5d-4f51-9372-62a7b80713c3 · outbound

This paper cites Harrigan, Kevin J.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Harrigan, Kevin J

Reference 18

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Observation a1dcbc98-88ec-44a7-bb6a-025f2c39b947 · outbound

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Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Unresolved cited work

Reference 19

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This paper cites Quantum algorithms: A survey of applications and end-to-end complexities.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Quantum algorithms: A survey of applications and end-to-end complexities

Reference 20

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Observation 69525b2d-bf7e-4196-96b8-7ef857d3f39a · outbound

This paper cites Quantum optimization using variational algorithms on near-term quantum devices.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Quantum optimization using variational algorithms on near-term quantum devices

Reference 21

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This paper cites Love, Alán Aspuru-Guzik, and Jeremy L.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Love, Alán Aspuru-Guzik, and Jeremy L

Reference 22

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Observation a897d10f-aec6-4eb3-8c84-ff4abfc9c21d · outbound

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

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Quantum Computing in the NISQ era and beyond

Reference 23

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Observation 2b37f258-497c-4dd7-a491-37ec957932a1 · outbound

This paper cites Impact of Noise on the Resilience and the Security of Quantum Computing.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Impact of Noise on the Resilience and the Security of Quantum Computing

Reference 24

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Observation d1586b26-0e9e-4b3c-8a1f-df5daaad4bb0 · outbound

This paper cites Quantum-Classical Computation of Schwinger Model Dynamics using Quantum Computers.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Quantum-Classical Computation of Schwinger Model Dynamics using Quantum Computers

Reference 25

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Observation 8e8d3a30-bc5a-4a66-b16c-f7c7a765c1ac · outbound

This paper cites Robustness Certificates for Sparse Adversarial Attacks by Randomized Ablation.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Robustness Certificates for Sparse Adversarial Attacks by Randomized Ablation

Reference 26

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Observation 3084aebd-5dd2-4ba3-84ce-d4a6e1e4a21e · outbound

This paper cites Svore, and Nathan Wiebe.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Svore, and Nathan Wiebe

Reference 27

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Observation 3fce3829-ed9f-4135-922c-406f3e0c1675 · outbound

This paper cites Ro- bustness Verification with Non-Uniform Ran- 8 domized Smoothing, 2021.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Ro- bustness Verification with Non-Uniform Ran- 8 domized Smoothing, 2021

Reference 28

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Observation 1ad504ec-564e-4027-b880-93f37cb9851f · outbound

This paper cites Opti- mal quantum circuits for general two-qubit gates.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Opti- mal quantum circuits for general two-qubit gates

Reference 29

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Observation faec14e2-1b7c-4fb1-b8b5-f9abc91dc2d1 · outbound

This paper cites Optimal provable ro- bustness of quantum classification via quan- tum hypothesis testing.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Optimal provable ro- bustness of quantum classification via quan- tum hypothesis testing

Reference 30

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Observation a2e44e0a-3258-4949-a54f-86a47380c958 · outbound

This paper cites Denoised Smoothing: A Provable Defense for Pretrained Classifiers.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Denoised Smoothing: A Provable Defense for Pretrained Classifiers

Reference 31

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This paper cites Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers

Reference 32

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Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Natural Evolution Strategies

Reference 33

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Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Unresolved cited work

Reference 34

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This paper cites Randomized Smoothing of All Shapes and Sizes.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Randomized Smoothing of All Shapes and Sizes

Reference 35

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This paper cites Robust Quantum Gates against Correlated Noise in Integrated Quantum Chips.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Robust Quantum Gates against Correlated Noise in Integrated Quantum Chips

Reference 36

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This paper cites Publisher: Nature Pub- lishing Group.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Publisher: Nature Pub- lishing Group

Reference 38

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This paper cites Kottmann, Thi Ha Kyaw, Bo Li, Alán Aspuru-Guzik, Ce Zhang, and Zhikuan Zhao.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Kottmann, Thi Ha Kyaw, Bo Li, Alán Aspuru-Guzik, Ce Zhang, and Zhikuan Zhao

Reference 39

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This paper cites West, Shu-Lok Tsang, Jia S.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise West, Shu-Lok Tsang, Jia S

Reference 40

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Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Unresolved cited work

Reference 46

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This paper cites (⊘ is hadamard division,·◦2 is element-wise square.) Proof.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise (⊘ is hadamard division,·◦2 is element-wise square.) Proof

Reference 47

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This paper cites URL https://link.aps.org/doi/10.1103/ PhysRevA.69.032315.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise URL https://link.aps.org/doi/10.1103/ PhysRevA.69.032315

Reference 2004

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This paper cites Certified Adversarial Robustness via Randomized Smoothing.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Certified Adversarial Robustness via Randomized Smoothing

Reference 2019

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This paper cites DOI: 10.1038/s41586- 022-04592-6.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise DOI: 10.1038/s41586- 022-04592-6

Reference 2022

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Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise Towards quantum enhanced adversarial robustness in machine learning

Reference 2023

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Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise URL https://www.nature.com/articles/ s41598-017-09098-0

Reference 2322

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This paper cites URLhttps:// www.nature.com/articles/nature23474.

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise URLhttps:// www.nature.com/articles/nature23474

Reference 4687

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Pith citing papers

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