Operator Learning for Schr\"{o}dinger Equation: Unitarity, Error Bounds, and Time Generalization

ArXi:2505.18288v2 Announce Type: replace-cross We consider the problem of learning the evolution operator for the time-dependent Schr\"{o}dinger equation, where the Hamiltonian may vary with time. Existing neural network-based surrogates often ignore fundamental properties of the Schr\"{o}dinger equation, such as linearity and unitarity, and lack theoretical guarantees on prediction error or time generalization. To address this, we