We quantify the emergent complexity of quantum states near quantum critical points on regular 1D lattices, via complex network measures based on quantum mutual information as the adjacency matrix, in direct analogy to quantifying the complexity of EEG/fMRI measurements of the brain. Using matrix product state methods, we show that network density, clustering, disparity, and Pearson's correlation obtain the critical point for both quantum Ising and Bose-Hubbard models to a high degree of accuracy in finite-size scaling for three classes of quantum phase transitions, Z2, mean field superfluid/Mott insulator, and a BKT crossover.Classical statistical physics has developed a powerful set of tools for analyzing complex systems, chief among them complex networks, in which connectivity and topology predominate over other system features [1]. Complex networks model systems as diverse as the brain and the internet; however, up till now they have been obtained in quantum systems by explicitly enforcing complex network structure in their quantum connections [2][3][4][5][6][7], e.g. entanglement percolation on a complex network [4]. In contrast, complexity measures on the brain observe emergent complexity arising out of, e.g., a regular array of EEG electrodes placed on the scalp, via an adjacency matrix formed from the classical mutual information calculated between them [8]. We apply the quantum generalization of this measure, an adjacency matrix of the quantum mutual information calculated on quantum states [9], to well known quantum many-body models on regular 1D lattices, and uncover emergent quantum complexity which clearly identifies quantum critical points (QCPs) [10,11]. Quantum mutual information bounds two-point correlations from above [12], measurable in a precise and tunable fashion in e.g. atom interferometry in 1D Bose gases [13], among many other quantum simulator architectures. Using matrix product state (MPS) computational methods [14,15], we demonstrate rapid finite size-scaling for both transverse Ising and Bose-Hubbard models, including Z 2 , mean field, and BKT quantum phase transitions.As we move toward more and more complex quantum systems in materials design and quantum simulators, involving a hierarchy of scales, diverse interacting components, and a structured environment, we expect to observe long-lived dynamical features, fat-tailed distributions, and other key identifiers of complexity [16][17][18]. Such systems include quantum simulator technologies based on ultracold atoms and molecules [19], trapped ions [20], and Rydberg gases [21], as well as superconducting Josephson-junction based nanoelectromechanical systems in which different quantum subsystems form compound quantum machines with both electrical and mechanical components [22]. A key area in which we have taken a first step beyond phase diagrams and ground state properties is non-equilibrium quantum dynamics, where critical exponents and renormalization group theory are only weakly applicable at best, e.g. in the KibbleZurek mechanism,...