Differentiable high-fidelity vectorial propagator
The numerical accuracy and computational efficiency of free-space field propagation directly determine what physics can be predicted and what devices can be designed, from nanophotonic metasurfaces to optically addressed quantum processors. Existing methods face a fundamental accuracy-speed trade-off, which is further compounded in the vectorial regime, where major formalisms remain fragmented with no unified computational framework. Here we address both challenges: a theory that unifies the vectorial diffraction framework, and an algorithm that resolves the accuracy-speed trade-off, each reinforcing the other. We prove that all major vectorial diffraction formalisms and vector-potential seeding methods arise from a single surface equivalence principle and reduce, without approximation, to a finite sequence of scalar angular-spectrum operations. Any advance in scalar propagation can therefore be exploited across the entire vectorial ecosystem. The error-compensating angular spectrum method (E-ASM) serves as a fast and accurate scalar solver, delivering peak signal-to-noise-ratio gains exceeding 60 dB over the state-of-the-art band-extended angular spectrum method at order-of-magnitude faster speed, with a freely configurable observation window. Their combination, implemented in a GPU-accelerated, fully differentiable form, turns rigorous vectorial field computation, previously prohibitive for higher-order beams over macroscopic distances, into a practical, inverse-design-ready tool. We demonstrate the framework on optically addressed local qubit gates and on vectorial Hermite-Gaussian and Laguerre-Gaussian beam propagation, resolving field structures inaccessible to scalar or approximate vectorial methods.