Rotationally equivariant quantum models can rely on vulnerable invariant statistics such as ring-averaged intensities, leaving them susceptible to classical transfer attacks, but suppressing the associated symmetry sectors substantially improves robustness.
IIA, the groupG=Z 𝑁𝜙 with𝑁 𝜙 =2 𝑛orb acts as discrete rotations (cyclic shifts) on the orbital register
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Feature-level analysis and adversarial transfer in rotationally equivariant quantum machine learning
Rotationally equivariant quantum models can rely on vulnerable invariant statistics such as ring-averaged intensities, leaving them susceptible to classical transfer attacks, but suppressing the associated symmetry sectors substantially improves robustness.