Gabriel V. Turturica and Violeta Iancu [ https://www.nature.com/articles/s41598-023-34679-7 ]
In the context of nuclear warhead verification, this work proposes an algorithm that combines for the first time, nuclear resonance fluorescence, machine learning and homomorphic inference to achieve zero-knowledge verification. Two elements ensure the security of the protocol, the implementation of the template-based protocol at the architecture level, using Siamese networks, and the use of homomorphic
encryption at inference time. The results of this work are presented in two parts: an extensive 2D example demonstrating the capability of Siamese neural networks to address the warhead verification problem and a single-point analysis highlighting the advantages and current limitations of homomorphic inference.