Qiboopt

For the complete documentation on qiboopt, please refer to qiboopt.

Installation instructions

Install first the package dependencies with the following commands.

We recommend to start with a fresh virtual environment to avoid dependency conflicts with previously installed packages.

   $ python -m venv ./env
   source activate ./env/bin/activate

The qiboopt package can be installed through pip:

   pip install qiboopt

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.

Formuating 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:

	from qiboopt.opt_class.opt_class import QUBO
	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()

Use QAOA to solve the QUBO problems (qp):

	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)

Use XQAOA to solve the QUBO problems (qp):

	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)

Use QAOA to solve the QUBO problems (qp) using Conditional Variance at Risk (CVaR) loss function:

	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)