Novel approaches to interacting and disordered systems

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Disordered quantum systems continue to play a key role in theoretical developments of modern condensed matter physics. Originally, this is due to Anderson's realization in 1958 that the quantum wave nature of condensed matter can lead to interference effects that suppress the classically expected charge transport mechanisms - the birth of Anderson localization. Already in Anderson's paper, it was discussed how the situation might be influenced by many-body effects. In the last decade, following the increase in computational power to allow the construction of meaningfully large Hilbert spaces even for interacting systems, this interplay of disorder and many-body physics has received much attention but remains restricted to small system sizes. What really is needed is a novel approach at solving the underlying Schrödinger equations. Recently, machine learning and deep learning have emerged as numerical techniques that use strategies of artificial intelligence to predict outcomes of numerical experiments. It therefore seems possible to use DL techniques to speed up such sparse matrix codes to construct well-approximated eigenstates for very large system size.