Quantum-Inspired Analysis of Neural Network Vulnerabilities: The Role of
  Conjugate Variables in System Attacks
Jun-Jie Zhang Deyu Meng
Neural networks demonstrate inherent vulnerability to small, non-random
perturbations, emerging as adversarial attacks. Such attacks, born from the
gradient of the loss function relative to the input, are discerned as input
conjugates, revealing a systemic fragility within the network structure.
Intriguingly, a mathematical congruence manifests between this mechanism and
the quantum physics' uncertainty principle, casting light on a hitherto
unanticipated interdisciplinarity. This inherent susceptibility within neural
network systems is generally intrinsic, highlighting not only the innate
vulnerability of these networks but also suggesting potential advancements in
the interdisciplinary area for understanding these black-box networks.