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一篇提出「去相干(decoherence)」概念的评论文章,论述 AI 大量生成的冗余内容如何让系统的技术债指数级膨胀,并认为这既排除了智能爆炸的可能,也带来了「去相干崩溃」的新风险。
This paper presents a rigorous N-qubit theory of stochastic quantum neural networks (SQNNs) for adversarially robust network intrusion detection, proving a decoherence-contraction theorem and showing that depolarising noise provides robustness against adversarial attacks, with experiments on the NSL-KDD dataset.