This is an attempt to use quantum annealing to process and evolve a Conway's Game of Life board.

The entire board is put as a constraint satisfaction problem using binary quadratic model (from dwave systems). And solved with multiple annealing methods (including dwave's quantum annealer).

I personally used a folder called working_folder to store all the input/output/image files (thus the reference in gitignore and some scripts)

labels.py is a library to supply uniform name generators for the variables in the BQM.

input_creator.py is designed to create input files for the QGOL.py

QGOL.py takes input file and solves it.

display_state_file.py is used to show the output, with both command line and graphical representation.

tool.py is just a random script file in case I want to test anything out.

non_annealing_solver.py is a legacy script to produce an output that simulates how a correct version of my old approach would work.

With 2 time steps only, forward evolution of the board. Completely accurate solution with classical or hybrid solvers. Completely accurate solution with quantum solver of Zephyr topology (tested on a small range of scenarios due to the size limit of the protoptype machines of this topology). Coarse solution with quantum solver of Pegasus topology.

Anything else is just broken (basically).

The problem formulation is by no means efficient, requiring 10 variables for a single cell on the board at a single time. Some variables can definitely be removed (I think).

Update non_annealing_solver.py to match the newer solution structure.

Revamp label system.