Quantum annealing

In parallel_machines the problems of scheduling on parallel machines are solved via QUBO and quantum (or simulated) annealing

Arguments:

• --case - default 1 - problem cases 1 to 5 determining various scheduling problems in increasing size
• --runs - default 1 - number of runs on quantum or simulated device
• --real - by default: False - use real annealing if True or simulated one if False
• --hyb - by default: False - use hybrid bqm solver if True
• --at - by default 1., annealing time [in \mu s] for real annealing
• --plot_item - by default 0, number of item to be plotted (items are feasible and sorted due to objective)
• --show_all -by default: False - show also not feasible solutions
• --psum - by default 100. - sum penalty
• --ppair - by default 100. - pair penalty
• --no_compute by default False, if True computation is not performed

Example use

Simulated annealing

python3 solve_problems.py --case 1 --no_runs 100 --psum 100 --ppair 50

python3 solve_problems.py --case 4 --no_runs 25 --psum 200 --ppair 100

Show all results also these that are not feasible

python3 solve_problems.py --case 4 --no_runs 25 --psum 200 --ppair 100 --show_all

Read file with data only:

python3 solve_problems.py --case 4 --no_runs 25 --psum 200 --ppair 100 --no_compute

Plot chart of --plot_item realisation

python3 solve_problems.py --case 1 --no_runs 25 --psum 200 --ppair 100 --plot_item 1

Exponential objective

python3 solve_problems.py --case 1 --no_runs 1000 --psum 10000 --ppair 10000 --exp

Real annealing

python3 solve_problems.py --case 1 --no_runs 4 --psum 100 --ppair 100 --real --at 1

Hybrid solver

python3 solve_problems.py --case 1 --psum 100 --ppair 100 --hyb.