QIRO MaxSetPacking#

Problem description#

Given a universe \([n]\) and \(m\) subsets \(\mathcal S = (S_j)^m_{j=1}\) , \(S_j \subset [n]\), find the maximum cardinality subcollection \(\mathcal S' \subset \mathcal S\) of pairwise disjoint subsets. Following the work of Hadfield et al., the MaxSetPacking problem is solved by solving the MaxIndepSet problem on the constraint graph \(G=(V,E)\) where vertices correspond to subsets in \(\mathcal S\) and edges correspond to pairs of intersecting subsets.

Transformation to MIS#

transform_max_set_pack_to_mis(problem)[source]#

Transforms a Maximum Set Packing problem instance into a Maximum Independent Set (MIS) problem instance.

Parameters:

problemlist[set]: A list of sets specifying the problem.

Returns:

Gnx.Graph: The corresponding graph to be solved by an MIS implementation.

Example implementation#

from qrisp import QuantumVariable
from qrisp.qiro import QIROProblem, create_max_indep_replacement_routine, create_max_indep_cost_operator_reduced, qiro_rx_mixer, qiro_init_function
from qrisp.qaoa import create_max_indep_set_cl_cost_function
import matplotlib.pyplot as plt
import networkx as nx

# sets are given as list of sets
sets = [{0,7,1},{6,5},{2,3},{5,4},{8,7,0},{2,4,7},{1,3,4},{7,9},{1,9},{1,3,8},{4,9},{0,7,9},{0,4,8},{1,4,8}]

G = transform_max_set_pack_to_mis(sets)
qarg = QuantumVariable(G.number_of_nodes())

qiro_instance = QIROProblem(G,
                            replacement_routine=create_max_indep_replacement_routine,
                            cost_operator=create_max_indep_cost_operator_reduced,
                            mixer=qiro_rx_mixer,
                            cl_cost_function=create_max_indep_set_cl_cost_function,
                            init_function=qiro_init_function
                            )
res_qiro = qiro_instance.run_qiro(qarg=qarg, depth=3, n_recursions=2)

That’s it! In the following, we print the 5 most likely solutions together with their cost values, and compare to the NetworkX solution.

cl_cost = create_max_indep_set_cl_cost_function(G)

print("5 most likely QIRO solutions")
max_five_qiro = sorted(res_qiro, key=res_qiro.get, reverse=True)[:5]
for res in max_five_qiro:
    print([sets[index] for index, value in enumerate(res) if value == '1'])
    print(cl_cost({res : 1}))

print("Networkx solution")
print([sets[index] for index in nx.approximation.maximum_independent_set(G)])