Sergio Sánchez Ramírez

In this talk, I present a collection of Julia packages developed for Tensor Network simulation experiments ([Tenet.jl](https://github.com/bsc-quantic/Tenet.jl) and [EinExprs.jl](https://github.com/bsc-quantic/EinExprs.jl)). We examine which Julia features and design choices enabled us to offer an intuitive interface for users, increasing the tunability and flexibility without loss of performance.

In this talk, I present the Julia library ecosystem that we have developed at the Barcelona Supercomputing Center for large-scale tensor network simulations. Specifically, I present:

- [**Tenet.jl**](https://github.com/bsc-quantic/Tenet.jl), a composable Tensor Network library that allows user for tunable executions. Its design has been carefully crafted to provide great expressibility, flexibility and performance.
- [**EinExprs.jl**](https://github.com/bsc-quantic/EinExprs.jl), a contraction path search library that offers state-of-art heuristics, visualization utilities and optimizers. It powers Tenet but the constructions introduced in it can be of use in other libraries.

The talk counts with code examples and introductions to the topics for users outside of the field. I will give an example of the expressive power of **Tenet** and **EinExprs** by showing how Google's quantum ~supremacy~ advantage experiment can be recreated in <15 lines of code.

Sergio Sánchez Ramírez

Talks:

Tenet.jl: Composable Tensor Network Library

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