mlsanityData to 14 Oct 2026

Towards quantum enhanced adversarial robustness in machine learning

arXiv 2306.12688 ↗First posted 22 Jun 2023quant-ph cs.AI cs.ETcited by 28

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Abstract (arXiv)

Machine learning algorithms are powerful tools for data driven tasks such as image classification and feature detection, however their vulnerability to adversarial examples - input samples manipulated to fool the algorithm - remains a serious challenge. The integration of machine learning with quantum computing has the potential to yield tools offering not only better accuracy and computational efficiency, but also superior robustness against adversarial attacks. Indeed, recent work has employed quantum mechanical phenomena to defend against adversarial attacks, spurring the rapid development of the field of quantum adversarial machine learning (QAML) and potentially yielding a new source of quantum advantage. Despite promising early results, there remain challenges towards building robust real-world QAML tools. In this review we discuss recent progress in QAML and identify key challenges. We also suggest future research directions which could determine the route to practicality for QAML approaches as quantum computing hardware scales up and noise levels are reduced.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). Builds on 55 of its 98 references that are in the map. First posted on arXiv 22 Jun 2023.

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55 of 98 references.

References · 55

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  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
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38 more
  1. 2017Evasion Attacks against Machine Learning at Test Timecited by 689
  2. 2017ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Modelscited by 666
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Citing papers per year: 2023: 4; 2024: 9; 2025: 10; 2026: 5.

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  1. 2024Efficient Generation of Targeted and Transferable Adversarial Examples for Vision-Language Models Via Diffusion Modelscited by 38
  2. 2023Training robust and generalizable quantum modelscited by 21
  3. 2023Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applicationscited by 14
  4. 2024A Comparative Analysis of Adversarial Robustness for Quantum and Classical Machine Learning Modelscited by 12
  5. 2024Quantum-Trained Convolutional Neural Network for Deepfake Audio Detectioncited by 12
  6. 2024Adversarial Robustness Guarantees for Quantum Classifierscited by 9
  7. 2025Distributionally Robust Free Energy Principle for Decision-Makingcited by 4
  8. 2025Q-Detection: A Quantum-Classical Hybrid Poisoning Attack Detection Methodcited by 3
  9. 2025SoK: Critical Evaluation of Quantum Machine Learning for Adversarial Robustnesscited by 3
  10. 2023Radio Signal Classification by Adversarially Robust Quantum Machine Learningcited by 2
  11. 2024A Modified Depolarization Approach for Efficient Quantum Machine Learningcited by 2
  12. 2024Security Concerns in Quantum Machine Learning as a Servicecited by 2
16 more
  1. 2024Self-Adaptive Quantum Kernel Principal Components Analysis for Compact Readout of Chemiresistive Sensor Arrayscited by 2
  2. 2024Quantum Kernel Learning for Small Dataset Modeling in Semiconductor Fabrication: Application to Ohmic Contactcited by 2
  3. 2025Adversarial Robustness of Partitioned Quantum Classifierscited by 2
  4. 2025Adversarial Robustness in Distributed Quantum Machine Learningcited by 2
  5. 2024Uncertainty-Aware Hardware Trojan Detection Using Multimodal Deep Learningcited by 1
  6. 2025Superior resilience to poisoning and amenability to unlearning in quantum machine learningcited by 1
  7. 2023Robust and Efficient Interference Neural Networks for Defending Against Adversarial Attacks in ImageNetcited by 0
  8. 2025Detecting underdetermination in parameterized quantum circuitscited by 0
  9. 2025Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noisecited by 0
  10. 2025The interplay of robustness and generalization in quantum machine learningcited by 0
  11. 2025Quantum Artificial Intelligence for Secure Autonomous Vehicle Navigation: An Architectural Proposalcited by 0
  12. 2026QuanForge: A Mutation Testing Framework for Quantum Neural Networkscited by 0
  13. 2026Defending Quantum Classifiers against Adversarial Perturbations through Quantum Autoencoderscited by 0
  14. 2026Decoherence as Defence and the Magnitude of Noise Regularisation: A Rigorous N -Qubit Theory of Stochastic Quantum Neural Networks for Adversarially Robust Network Intrusion Detectioncited by 0
  15. 2026Benchmarking Quantum Machine Learning for Power-System Attack Detection: Evaluation Choices Decide the Outcome Before the Models Docited by 0
  16. 2026When Normalization Selects the Sign: Auditing Robustness Ablations in Quantum Attentioncited by 0