  Radio signal classification plays a pivotal role in identifying the
modulation scheme used in received radio signals, which is essential for
demodulation and proper interpretation of the transmitted information.
Researchers have underscored the high susceptibility of ML algorithms for radio
signal classification to adversarial attacks. Such vulnerability could result
in severe consequences, including misinterpretation of critical messages,
interception of classified information, or disruption of communication
channels. Recent advancements in quantum computing have revolutionized theories
and implementations of computation, bringing the unprecedented development of
Quantum Machine Learning (QML). It is shown that quantum variational
classifiers (QVCs) provide notably enhanced robustness against classical
adversarial attacks in image classification. However, no research has yet
explored whether QML can similarly mitigate adversarial threats in the context
of radio signal classification. This work applies QVCs to radio signal
classification and studies their robustness to various adversarial attacks. We
also propose the novel application of the approximate amplitude encoding (AAE)
technique to encode radio signal data efficiently. Our extensive simulation
results present that attacks generated on QVCs transfer well to CNN models,
indicating that these adversarial examples can fool neural networks that they
are not explicitly designed to attack. However, the converse is not true. QVCs
primarily resist the attacks generated on CNNs. Overall, with comprehensive
simulations, our results shed new light on the growing field of QML by bridging
knowledge gaps in QAML in radio signal classification and uncovering the
advantages of applying QML methods in practical applications.
