mlsanityData to 14 Oct 2026

Certified Robustness of Quantum Classifiers against Adversarial Examples through Quantum Noise

arXiv 2211.00887 ↗First posted 2 Nov 2022quant-ph cs.LG cs.NEcited by 18

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Abstract (arXiv)

Recently, quantum classifiers have been found to be vulnerable to adversarial attacks, in which quantum classifiers are deceived by imperceptible noises, leading to misclassification. In this paper, we propose the first theoretical study demonstrating that adding quantum random rotation noise can improve robustness in quantum classifiers against adversarial attacks. We link the definition of differential privacy and show that the quantum classifier trained with the natural presence of additive noise is differentially private. Finally, we derive a certified robustness bound to enable quantum classifiers to defend against adversarial examples, supported by experimental results simulated with noises from IBM's 7-qubits device.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 14 of its 26 references that are in the map. First posted on arXiv 2 Nov 2022.

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14 of 26 references.

References · 14

  1. 2019Certified Adversarial Robustness via Randomized Smoothingcited by 1,340
  2. 2018Efficient Neural Network Robustness Certification with General Activation Functionscited by 445
  3. 2019Parameterized quantum circuits as machine learning modelscited by 332
  4. 2016Quantum Machine Learningcited by 326
  5. 2019Efficient Learning for Deep Quantum Neural Networkscited by 161
  6. 2018Quantum generative adversarial networkscited by 128
  7. 2018Towards Quantum Machine Learning with Tensor Networkscited by 76
  8. 2020Quantum noise protects quantum classifiers against adversariescited by 68
  9. 2020Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognitioncited by 57
  10. 2022When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computingcited by 40
  11. 2021Recent advances for quantum classifierscited by 35
  12. 2022Theoretical Error Performance Analysis for Variational Quantum Circuit Based Functional Regressioncited by 26
2 more
  1. 2020Optimal Provable Robustness of Quantum Classification via Quantum Hypothesis Testingcited by 25
  2. 2021Classical-to-Quantum Transfer Learning for Spoken Command Recognition Based on Quantum Neural Networkscited by 19

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 7; 2025: 5; 2026: 3.

18 papers.

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  1. 2023Quantum Pufferfish Privacy: A Flexible Privacy Framework for Quantum Systemscited by 9
  2. 2023A unifying framework for differentially private quantum algorithmscited by 7
  3. 2024Contraction of Private Quantum Channels and Private Quantum Hypothesis Testingcited by 7
  4. 2023Quantum Neural Networks under Depolarization Noise: Exploring White-Box Attacks and Defensescited by 5
  5. 2024Generating Universal Adversarial Perturbations for Quantum Classifierscited by 5
  6. 2024Constructing Optimal Noise Channels for Enhanced Robustness in Quantum Machine Learningcited by 5
  7. 2024A Modified Depolarization Approach for Efficient Quantum Machine Learningcited by 2
  8. 2024Discrete Randomized Smoothing Meets Quantum Computingcited by 2
  9. 2025Adversarial Robustness of Partitioned Quantum Classifierscited by 2
  10. 2025Adversarial Robustness in Distributed Quantum Machine Learningcited by 2
  11. 2024Quadratic Advantage with Quantum Randomized Smoothing Applied to Time-Series Analysiscited by 1
  12. 2024Certifiably Robust Encoding Schemescited by 1
6 more
  1. 2025On the Generalization of Adversarially Trained Quantum Classifierscited by 1
  2. 2025Quantum Artificial Intelligence for Mission-Critical Systems: Foundations, Architectural Elements, and Future Directionscited by 1
  3. 2025Detecting underdetermination in parameterized quantum circuitscited by 0
  4. 2026QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuitscited by 0
  5. 2026Representational separation between unitary and channel quantum generative models via shared classical randomness at shallow depthcited by 0
  6. 2026When Normalization Selects the Sign: Auditing Robustness Ablations in Quantum Attentioncited by 0