  The sliced Wasserstein distance as well as its variants have been widely
considered in comparing probability measures defined on $\mathbb R^d$. Here we
derive the notion of sliced Wasserstein distance for measures on an infinite
dimensional separable Hilbert spaces, depict the relation between sliced
Wasserstein distance and narrow convergence of measures and quantize the
approximation via empirical measures.
