Association between slowly varying rs-fMRI networks and rapidly varying topographical EEG microstates is yet to be explored. We propose a novel quantum microstate-based clustering approach to assess quasi-stable patterns in resting state-EEG signals and analyze their association with rs-fMRI networks (RSNs). Clustering results were estimated by constructing a scale-space probability function from dynamic EEG potentials and the eigenstates in Hilbert space was assessed. These quantum-EEG microstate informed RSNs exhibited correlation with frontoparietal, posterior DMN, executive, visual and sensori-motor networks. The sub-second coherent neural activation within global large-scale functional brain networks may help investigate different mental health states.