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.

翻译：对抗样本在机器学习中已成为研究焦点，因其能以看似微不足道的输入扰动欺骗模型，可能导致严重后果。在本研究中，我们对对抗样本的产生进行了深入探究，这一现象原则上可广泛出现在各类机器学习模型中。通过研究，我们揭示了一个称为计算纠缠的新概念，其能够纠缠远距离特征，无论空间距离如何都能展现完美相关或反相关性，这对对抗样本的产生起到了关键作用。我们阐述了计算纠缠如何与相对论效应（如时间膨胀和长度收缩）一样作用于特征对，最终导致其角度差和距离趋于零（表示完美相关）或趋于最大值（表示完美反相关）。