Circumventing query complexity barriers in learning quantum dynamics via physics-informed kernels
Learning continuous quantum dynamical trajectories—essential for understanding non-equilibrium phenomena in quantum chemistry and condensed matter physics—remains prohibitively expensive on quantum computers. Conventional data-driven surrogates treat observables as generic time series, ignoring the governing Schrödinge...