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Constructing Optimal Noise Channels for Enhanced Robustness in Quantum Machine Learning

Created by
  • Haebom

Author

David Winderl, Nicola Franco, Jeanette Miriam Lorenz

Outline

In this paper, we investigate the relationship between quantum noise channels and differential privacy (DP) as a way to enhance the security against adversarial attacks on quantum machine learning (QML) models. We present this relationship by constructing a set of noise channels called $(\alpha, \gamma)$-channels, which are essentially ε-DPs. Through this, we successfully replicate the ε-DP bounds observed in depolarization and random rotation channels, verifying the generality of our framework. Furthermore, we construct optimally robust channels using semidefinite programs, and show through small-scale experimental evaluation that using optimal noise channels rather than depolarization noise is useful in improving the adversarial accuracy. Finally, we evaluate the effects of variables α and γ on the certifiable robustness and the effects of different encoding methods on the robustness of the classifier.

Takeaways, Limitations

Takeaways:
A method for ensuring differential privacy for QML models using quantum noise channels is presented.
We generalize the ε-DP bound for various noise channels by introducing a new set of noise channels, called $(\alpha, \gamma)$-channels.
Optimal robust noise channel configuration and performance verification using a semi-definition program.
Presenting the possibility of improving the robustness of QML models against adversarial attacks.
Analysis of the impact of encoding methods on the robustness of QML models.
Limitations:
Small-scale experimental evaluations limit generalizability to large-scale real-world applications.
Absence of clear guidelines for choosing optimal parameters for the $(\alpha, \gamma)$-channel.
Lack of comprehensive evaluation of various adversarial attack techniques.
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