  Adversarial examples in machine learning has emerged as a focal point of
research due to their remarkable ability to deceive models with seemingly
inconspicuous input perturbations, potentially resulting in severe
consequences. In this study, we undertake a thorough investigation into the
emergence of adversarial examples, a phenomenon that can, in principle,
manifest in a wide range of machine learning models. Through our research, we
unveil a new notion termed computational entanglement, with its ability to
entangle distant features, display perfect correlations or anti-correlations
regardless to their spatial separation, significantly contributes to the
emergence of adversarial examples. We illustrate how computational entanglement
aligns with relativistic effects such as time dilation and length contraction
to feature pair, ultimately resulting in the convergence of their angle
differences and distances towards zero, signifying perfect correlation, or
towards maximum, indicating perfect anti-correlation.
