Qiskit Optimization is an open-source framework that covers the whole range from high-level modeling of optimization problems, with automatic conversion of problems to different required representations, to a suite of easy-to-use 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.

• API reference

We encourage installing Qiskit via the pip tool (a python package manager), which installs all Qiskit elements, including Optimization.

pip install qiskit

pip will handle all dependencies automatically and you will always install the latest (and well-tested) version.

If you want to work on the very latest work-in-progress 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.

*Note: tutorials are undergoing revision and re-organization. Hence you may notice some content you will see referenced is under legacy_tutorials pending such re-work.

• IBM CPLEX may be installed using pip install qiskit-optimization[cplex] to allow the use of the CplexOptimizer classical solver algorithm, as well as enabling the reading of LP files.

Now that Qiskit is installed, it's time to begin working with the optimization module. Let's try an optimization experiment to compute the solution of a Max-Cut. The Max-Cut 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 import BasicAer
from qiskit.algorithms import QAOA
from qiskit.algorithms.optimizers import SPSA

# 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('x{}'.format(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 max-cut solution
problem.linear_constraint([1, 0, 0, 0], '==', 1)

# Run quantum algorithm QAOA on qasm simulator
spsa = SPSA(max_trials=250)
backend = BasicAer.get_backend('qasm_simulator')
qaoa = QAOA(optimizer=spsa, p=5, quantum_instance=backend)
algorithm = MinimumEigenOptimizer(qaoa)
result = algorithm.solve(problem)
print(result)  # prints solution, x=[1, 0, 1, 0], the cost, fval=4

Learning path notebooks may be found in the optimization tutorials section of the documentation and are a great place to start.

Jupyter notebooks containing further examples, for the optimization module, may be found in the following Qiskit GitHub repositories at qiskit-community-tutorials/optimization.

You can also use Qiskit to execute your code on a real quantum chip. In order to do so, you need to configure Qiskit to use the credentials in your IBM Quantum Experience account. For more detailed information refer to the relevant instructions in the Qiskit Terra GitHub repository .

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.

Now you're set up and ready to check out some of the other examples from the Qiskit Tutorials repository, that are used for the IBM Quantum Experience, and from the Qiskit Community Tutorials.

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.

This project uses the Apache License 2.0.