  Tensor Networks are graph representations of summation expressions in which
vertices represent tensors and edges represent tensor indices or vector spaces.
In this work, we present EinExprs.jl, a Julia package for contraction path
optimization that offers state-of-art optimizers. We propose a representation
of the contraction path of a Tensor Network based on symbolic expressions.
Using this package the user may choose among a collection of different methods
such as Greedy algorithms, or an approach based on the hypergraph partitioning
problem. We benchmark this library with examples obtained from the simulation
of Random Quantum Circuits (RQC), a well known example where Tensor Networks
provide state-of-the-art methods.
