<code style="color:#0065BD">Netzwerk</code>

A collection of state-of-the-art contraction ordering algorithms. Plug-in for opt_einsum and cotengra.

• Optimal and near-optimal algorithms for several classes of tensor networks
• Based on the latest research in contraction ordering and database join ordering
• Careful C++ implementations

Description

$\large \textcolor{#0065bd}{\texttt{Netzwerk}}$ hosts optimal and near-optimal algorithms for tensor network contraction ordering:

• $\large \texttt{TensorIKKBZ}$: Optimal linear contraction orders for tree tensor networks
• $\large \texttt{LinDP}$: Optimal general contraction trees given the linear contraction orders of $\large \texttt{TensorIKKBZ}$
• Coming soon..

Setup

Build the shared library, which requires CMake.

cd src/netzwerk
mkdir -p build
cd build
cmake ..
make

The following initializes the package, assuming you are still in build:

cd ../../
versioneer install
pip3 install .

Usage

Make sure that opt_einsum and cotengra are imported before netzwerk, as follows:

import opt_einsum as oe
import cotengra as ctg
import netzwerk

Data Generation

Example: generate FTPS of up to 100 tensors.

python3 gen.py -t ftps -s 100

The files will be stored in data/ftps.

Run

The script bench.sh runs the experiments. For instance, if you want to benchmark on the networks you have just generated:

./bench.sh opt_einsum ftps 100

The results will be stored in results. For the Sycamore circuit, simply run

./bench.sh cotengra sycamore

Plot

Use the following notebooks to plot the results:

• bench-plot.ipynb: for networks generated via gen.py
• circuit-plot.ipynb: for quantum circuits, e.g., Sycamore.

Citation

You can cite netzwerk as follows:

@article{tensorikkbz,
   title={{On the Optimal Linear Contraction Order of Tree Tensor Networks, and Beyond}},
   volume={46},
   ISSN={1095-7197},
   url={http://dx.doi.org/10.1137/23M161286X},
   DOI={10.1137/23m161286x},
   number={5},
   journal={SIAM Journal on Scientific Computing},
   publisher={Society for Industrial & Applied Mathematics (SIAM)},
   author={Stoian, Mihail and Milbradt, Richard M. and Mendl, Christian B.},
   year={2024},
   month=oct, pages={B647–B668}
}