Qiskit Optimization: A library of quantum computing optimizations
Project description
Qiskit Optimization
Qiskit Optimization is an opensource framework that covers the whole range from highlevel modeling of optimization problems, with automatic conversion of problems to different required representations, to a suite of easytouse quantum optimization algorithms that are ready to run on classical simulators, as well as on real quantum devices via Qiskit.
The Optimization module enables easy, efficient modeling of optimization problems using docplex. A uniform interface as well as automatic conversion between different problem representations allows users to solve problems using a large set of algorithms, from variational quantum algorithms, such as the Quantum Approximate Optimization Algorithm QAOA, to Grover Adaptive Search using the GroverOptimizer leveraging fundamental algorithms provided by Terra. Furthermore, the modular design of the optimization module allows it to be easily extended and facilitates rapid development and testing of new algorithms. Compatible classical optimizers are also provided for testing, validation, and benchmarking.
Installation
We encourage installing Qiskit Optimization via the pip tool (a python package manager).
pip install qiskitoptimization
pip will handle all dependencies automatically and you will always install the latest (and welltested) version.
If you want to work on the very latest workinprogress versions, either to try features ahead of their official release or if you want to contribute to Optimization, then you can install from source. To do this follow the instructions in the documentation.
Optional Installs

IBM CPLEX may be installed using
pip install 'qiskitoptimization[cplex]'
to enable the reading ofLP
files and the usage of theCplexOptimizer
, wrapper forcplex.Cplex
. Currently there is no python 3.9 version of CPLEX. In this case, the CPLEX install command will have no effect. 
CVXPY may be installed using the command
pip install 'qiskitoptimization[cvx]'
. CVXPY being installed will enable the usage of the GoemansWilliamson algorithm as an optimizerGoemansWilliamsonOptimizer
. 
Matplotlib may be installed using the command
pip install 'qiskitoptimization[matplotlib]'
. Matplotlib being installed will enable the usage of thedraw
method in the graph optimization application classes. 
Gurobipy may be installed using the command
pip install 'qiskitoptimization[gurobi]'
. Gurobipy being installed will enable the usage of the GurobiOptimizer.
Creating Your First Optimization Programming Experiment in Qiskit
Now that Qiskit Optimization is installed, it's time to begin working with the optimization module. Let's try an optimization experiment to compute the solution of a MaxCut. The MaxCut problem can be formulated as quadratic program, which can be solved using many several different algorithms in Qiskit. In this example, the MinimumEigenOptimizer is employed in combination with the Quantum Approximate Optimization Algorithm (QAOA) as minimum eigensolver routine.
import networkx as nx
import numpy as np
from qiskit_optimization import QuadraticProgram
from qiskit_optimization.algorithms import MinimumEigenOptimizer
from qiskit.utils import algorithm_globals, QuantumInstance
from qiskit import BasicAer
from qiskit.algorithms import QAOA
from qiskit.algorithms.optimizers import SPSA
seed = 1234
algorithm_globals.random_seed = seed
# Generate a graph of 4 nodes
n = 4
graph = nx.Graph()
graph.add_nodes_from(np.arange(0, n, 1))
elist = [(0, 1, 1.0), (0, 2, 1.0), (0, 3, 1.0), (1, 2, 1.0), (2, 3, 1.0)]
graph.add_weighted_edges_from(elist)
# Compute the weight matrix from the graph
w = nx.adjacency_matrix(graph)
# Formulate the problem as quadratic program
problem = QuadraticProgram()
_ = [problem.binary_var(f"x{i}") for i in range(n)] # create n binary variables
linear = w.dot(np.ones(n))
quadratic = w
problem.maximize(linear=linear, quadratic=quadratic)
# Fix node 0 to be 1 to break the symmetry of the maxcut solution
problem.linear_constraint([1, 0, 0, 0], '==', 1)
# Run quantum algorithm QAOA on qasm simulator
spsa = SPSA(maxiter=250)
backend = BasicAer.get_backend('qasm_simulator')
q_i = QuantumInstance(backend=backend, seed_simulator=seed, seed_transpiler=seed)
qaoa = QAOA(optimizer=spsa, reps=5, quantum_instance=q_i)
algorithm = MinimumEigenOptimizer(qaoa)
result = algorithm.solve(problem)
print(result) # prints solution, x=[1, 0, 1, 0], the cost, fval=4
Further examples
Learning path notebooks may be found in the optimization tutorials section of the documentation and are a great place to start.
Contribution Guidelines
If you'd like to contribute to Qiskit, please take a look at our contribution guidelines. This project adheres to Qiskit's code of conduct. By participating, you are expected to uphold this code.
We use GitHub issues for tracking requests and bugs. Please join the Qiskit Slack community and for discussion and simple questions. For questions that are more suited for a forum, we use the Qiskit tag in Stack Overflow.
Authors and Citation
Optimization was inspired, authored and brought about by the collective work of a team of researchers. Optimization continues to grow with the help and work of many people, who contribute to the project at different levels. If you use Qiskit, please cite as per the provided BibTeX file.
Please note that if you do not like the way your name is cited in the BibTex file then consult the information found in the .mailmap file.
License
This project uses the Apache License 2.0.
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