  Quantum machine learning uses principles from quantum mechanics to process
data, offering potential advances in speed and performance. However, previous
work has shown that these models are susceptible to attacks that manipulate
input data or exploit noise in quantum circuits. Following this, various
studies have explored the robustness of these models. These works focus on the
robustness certification of manipulations of the quantum states. We extend this
line of research by investigating the robustness against perturbations in the
classical data for a general class of data encoding schemes. We show that for
such schemes, the addition of suitable noise channels is equivalent to
evaluating the mean value of the noiseless classifier at the smoothed data,
akin to Randomized Smoothing from classical machine learning. Using our general
framework, we show that suitable additions of phase-damping noise channels
improve empirical and provable robustness for the considered class of encoding
schemes.
