animeshnanda1 / exploiting_symmetry_reduces_the_cost_of_training_qaoa

Code and data for the paper "Exploiting Symmetry Reduces the Cost of Training QAOA"

Code and data for the paper "Exploiting Symmetry Reduces the Cost of Training QAOA"

Dependencies

• NetworkX
• numpy
• sympy 1.7.1
• QTensor (tested as of commit 7a934d7)
• dreadnaut (nauty 2.7, can be downloaded here)

Running

Computing the symmetries of a graph

To compute the symmetries of a graph, use compute_orbits.py. compute_orbits.py runs dreadnaut internally. Set environment variable $DREADNAUT_PATH to be the path to dreadnaut installation, e.g. export DREADNAUT_PATH=~/soft/nauty27r1/dreadnaut

Example:

python compute_orbits.py --graph graphs/small_search_tree/latin-30 --outpath data/test

Computing the QAOA energy in serial

python compute_energy_serial.py --graph graphs/large_search_tree/mz-aug2-22 --outpath data/test -p 1 --seed 1

Computing the QAOA energy faster by exploiting symmetry

Replies on precomputed orbits (produced by compute_orbits.py)

python compute_energy_accelerated.py --graph graphs/large_search_tree/mz-aug2-22 --outpath data/test -p 1

Reproducing the figures and tables from the paper

See jupyter/generate_tables.ipynb. Additional dependencies: pandas, seaborn