Abstract: Machine-learning-based approaches, routinely adopted in cutting-edge industrial applications, are being increasingly adopted to study fundamental problems in science. Many-body physics is very much at the forefront of these exciting developments, given its intrinsic ​“big-data” nature. In this seminar, I will present selected applications to the quantum realm. First, I will discuss how a systematic, controlled machine learning of the many-body wave-function can be realized. This goal is achieved by a variational representation of quantum states based on artificial neural networks. I will then discuss applications in diverse domains, including prototypical open problems in condensed matter physics — fermions and frustrated spins — as well as applications to characterize and improve quantum hardware and software.