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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.