Quickstart

Once installed, qiboopt allows the general user to solve QUBO problems with the built-in QUBO class. Along with the QUBO class, there are some combinatorial classes found in qiboopt.combinatorial.

Formulating a QUBO problem:

• Maximal Independent Set:

import networkx as nx
from qiboopt.combinatorial.combinatorial import MIS

g = nx.Graph()
g.add_edges_from([(0, 1), (1, 2), (2, 0)])
mis = MIS(g)
penalty = 10
qp = mis.penalty_method(penalty)

• Shortest Vector Problem:

Qdict = {(0, 0): 1.0, (0, 1): 0.5, (1, 1): -1.0}
qp = QUBO(0, Qdict)

# Brute force search by evaluating all possible binary vectors.
opt_vector, min_value = qp.brute_force()

QUBO problems can be solved using the QAOA method:

from qiboopt.opt_class.opt_class import QUBO
# Train 2 layers of regular QAOA
gammas = [0.1, 0.2]
betas = [0.3, 0.4]
output = qp.train_QAOA(gammas=gammas, betas=betas)

or the more modern XQAOA approach:

from qiboopt.opt_class.opt_class import QUBO
# Train 2 layers of XQAOA
gammas = [0.1, 0.2]
betas = [0.3, 0.4]
alphas = [0.5, 0.6]
output = qp.train_QAOA(gammas=gammas, betas=betas, alphas=alphas)

The Conditional Variance at Risk (CVaR) can also be used as an alternative loss function in solving the QUBO problem:

from qiboopt.opt_class.opt_class import QUBO
# Train 2 layers of regular QAOA with CVaR
gammas = [0.1, 0.2]
betas = [0.3, 0.4]
output = qp.train_QAOA(gammas=gammas, betas=betas, regular_loss=False, cvar_delta=0.1)