In this chapter, we describe a tensor network (TN) based common language established between machine learning and many-body physics, which allows for bidirectional contributions. By showing that many-body wave functions are structurally equivalent to mappings of convolutional and recurrent networks, we bring forth quantum entanglement measures as natural quantifiers of dependencies modeled by such networks. Accordingly, we propose a novel entanglement-based deep learning design scheme that sheds light on the success of popular architectural choices made by deep learning practitioners and suggests new practical prescriptions. In the other direction, we construct TNs corresponding to deep recurrent and convolutional networks. This allows us to theoretically demonstrate that these architectures are powerful enough to represent highly entangled quantum systems polynomially more efficiently than previously employed architectures. We thus provide theoretical motivation to shift neural-network-based wave function representations closer to state-of-the-art deep learning architectures.

This chapter introduces the basic theoretical tools for handling many-body quantum systems. Starting from second quantized operators, we discuss how it is possible to describe the composite wavefunction of multi-particle systems, and discuss representations in various bases. The algebra of Fock states is described for single and multi-mode systems, and how they relate to the eigenstates of the Schrodinger equation. Finally, we describe how interactions between particles can be introduced in a general way, and then describe the most common type of interaction in cold atom systems, the s-wave interaction