The paper delivers the first comprehensive systematization of adversarial robustness in QML with new empirical tests showing an accuracy-robustness trade-off, amplitude encoding's vulnerability, and QML's greater susceptibility to evasion attacks than classical models.
Parameterized quantum circuits as machine learning models
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A 4-qubit quantum feature pyramid gating architecture raises mean IoU from 0.8404 to 0.9389 over classical addition in controlled ablations on the TGS salt segmentation dataset.
A mixture-of-experts hybrid quantum model achieves 0.793 average precision on credit card fraud detection compared to 0.770 for XGBoost, with modest extra inference time.
Fully-connected VQCs match quantum transformer performance on tabular data with far fewer parameters and better noise resilience.
Magnitude-only encoding reaches 99.57% accuracy on 3-class and 71.19% on 8-class SAR tasks in hybrid models, beating phase-inclusive alternatives, while phase boosts pure quantum models by up to 21.65 points.
citing papers explorer
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SoK: Critical Evaluation of Quantum Machine Learning for Adversarial Robustness
The paper delivers the first comprehensive systematization of adversarial robustness in QML with new empirical tests showing an accuracy-robustness trade-off, amplitude encoding's vulnerability, and QML's greater susceptibility to evasion attacks than classical models.
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Quantum Feature Pyramid Gating for Seismic Image Segmentation
A 4-qubit quantum feature pyramid gating architecture raises mean IoU from 0.8404 to 0.9389 over classical addition in controlled ablations on the TGS salt segmentation dataset.
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A Mixture-of-Experts Framework for Practical Hybrid-Quantum Models in Credit Card Fraud Detection
A mixture-of-experts hybrid quantum model achieves 0.793 average precision on credit card fraud detection compared to 0.770 for XGBoost, with modest extra inference time.
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Do Quantum Transformers Help? A Systematic VQC Architecture Comparison on Tabular Benchmarks
Fully-connected VQCs match quantum transformer performance on tabular data with far fewer parameters and better noise resilience.
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Magnitude Is All You Need? Rethinking Phase in Quantum Encoding of Complex SAR Data
Magnitude-only encoding reaches 99.57% accuracy on 3-class and 71.19% on 8-class SAR tasks in hybrid models, beating phase-inclusive alternatives, while phase boosts pure quantum models by up to 21.65 points.