LATIDIA · Ciberseguridad

La encriptabilidad como opción de coordenadas: aprendizaje federado homomórfico de profundidad uno de redes neuronales cuánticas

arXiv: 2609.30581v1Announce Type: cross Resumen: La capacitación encriptada se basa en mantener las actualizaciones del lado del servidor en un nivel bajo. Esta restricción tradicionalmente excluye los modelos cuyos pesos habitan en un grupo de Lie compacto (notablemente vari

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arXiv:2609.30581v1 Announce Type: cross Abstract: Encrypted training relies on keeping server-side updates low-degree. This constraint traditionally excludes models whose weights inhabit a compact Lie group (notably variational quantum circuits, where every trainable weight is an $\mathrm{SU(2)}$ rotation). Expressed in Euler angles or discrete alphabets, these updates appear transcendental, historically demanding prohibitive costs: one client--server round per gate, or upwards of $25{,}000$ operations per weight. This penalty is strictly an artefact of coordinates. In the unit-quaternion (spin) chart, group composition is exactly bilinear (degree two, with coefficients in $\{-1,0,+1\}$). Consequently, encrypted rotation updates cost one multiplicative level and federated averaging costs zero in any levelled homomorphic scheme, completely eliminating bootstrapping. This