%global _empty_manifest_terminate_build 0 Name: python-qiskit-optimization Version: 0.5.0 Release: 1 Summary: Qiskit Optimization: A library of quantum computing optimizations License: Apache-2.0 URL: https://github.com/Qiskit/qiskit-optimization Source0: https://mirrors.nju.edu.cn/pypi/web/packages/c0/a3/9084490d4fbc79ef84e733ccdc706613f248f12252b574f083dacdd9032a/qiskit-optimization-0.5.0.tar.gz BuildArch: noarch Requires: python3-qiskit-terra Requires: python3-scipy Requires: python3-numpy Requires: python3-docplex Requires: python3-setuptools Requires: python3-networkx Requires: python3-cplex Requires: python3-cvxpy Requires: python3-gurobipy Requires: python3-matplotlib %description ### Optional Installs * **IBM CPLEX** may be installed using `pip install 'qiskit-optimization[cplex]'` to enable the reading of `LP` files and the usage of the `CplexOptimizer`, wrapper for ``cplex.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 'qiskit-optimization[cvx]'`. CVXPY being installed will enable the usage of the Goemans-Williamson algorithm as an optimizer `GoemansWilliamsonOptimizer`. * **Matplotlib** may be installed using the command `pip install 'qiskit-optimization[matplotlib]'`. Matplotlib being installed will enable the usage of the `draw` method in the graph optimization application classes. * **Gurobipy** may be installed using the command `pip install 'qiskit-optimization[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 [Max-Cut](https://en.wikipedia.org/wiki/Maximum_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. ```python from docplex.mp.model import Model from qiskit_optimization.algorithms import MinimumEigenOptimizer from qiskit_optimization.translators import from_docplex_mp from qiskit.utils import algorithm_globals from qiskit.primitives import Sampler from qiskit.algorithms.minimum_eigensolvers import QAOA from qiskit.algorithms.optimizers import SPSA # Generate a graph of 4 nodes n = 4 edges = [(0, 1, 1.0), (0, 2, 1.0), (0, 3, 1.0), (1, 2, 1.0), (2, 3, 1.0)] # (node_i, node_j, weight) # Formulate the problem as a Docplex model model = Model() # Create n binary variables x = model.binary_var_list(n) # Define the objective function to be maximized model.maximize(model.sum(w * x[i] * (1 - x[j]) + w * (1 - x[i]) * x[j] for i, j, w in edges)) # Fix node 0 to be 1 to break the symmetry of the max-cut solution model.add(x[0] == 1) # Convert the Docplex model into a `QuadraticProgram` object problem = from_docplex_mp(model) # Run quantum algorithm QAOA on qasm simulator seed = 1234 algorithm_globals.random_seed = seed spsa = SPSA(maxiter=250) sampler = Sampler() qaoa = QAOA(sampler=sampler, optimizer=spsa, reps=5) algorithm = MinimumEigenOptimizer(qaoa) result = algorithm.solve(problem) print(result.prettyprint()) # prints solution, x=[1, 0, 1, 0], the cost, fval=4 ``` ### Further examples Learning path notebooks may be found in the [optimization tutorials](https://qiskit.org/documentation/optimization/tutorials/index.html) section %package -n python3-qiskit-optimization Summary: Qiskit Optimization: A library of quantum computing optimizations Provides: python-qiskit-optimization BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-qiskit-optimization ### Optional Installs * **IBM CPLEX** may be installed using `pip install 'qiskit-optimization[cplex]'` to enable the reading of `LP` files and the usage of the `CplexOptimizer`, wrapper for ``cplex.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 'qiskit-optimization[cvx]'`. CVXPY being installed will enable the usage of the Goemans-Williamson algorithm as an optimizer `GoemansWilliamsonOptimizer`. * **Matplotlib** may be installed using the command `pip install 'qiskit-optimization[matplotlib]'`. Matplotlib being installed will enable the usage of the `draw` method in the graph optimization application classes. * **Gurobipy** may be installed using the command `pip install 'qiskit-optimization[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 [Max-Cut](https://en.wikipedia.org/wiki/Maximum_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. ```python from docplex.mp.model import Model from qiskit_optimization.algorithms import MinimumEigenOptimizer from qiskit_optimization.translators import from_docplex_mp from qiskit.utils import algorithm_globals from qiskit.primitives import Sampler from qiskit.algorithms.minimum_eigensolvers import QAOA from qiskit.algorithms.optimizers import SPSA # Generate a graph of 4 nodes n = 4 edges = [(0, 1, 1.0), (0, 2, 1.0), (0, 3, 1.0), (1, 2, 1.0), (2, 3, 1.0)] # (node_i, node_j, weight) # Formulate the problem as a Docplex model model = Model() # Create n binary variables x = model.binary_var_list(n) # Define the objective function to be maximized model.maximize(model.sum(w * x[i] * (1 - x[j]) + w * (1 - x[i]) * x[j] for i, j, w in edges)) # Fix node 0 to be 1 to break the symmetry of the max-cut solution model.add(x[0] == 1) # Convert the Docplex model into a `QuadraticProgram` object problem = from_docplex_mp(model) # Run quantum algorithm QAOA on qasm simulator seed = 1234 algorithm_globals.random_seed = seed spsa = SPSA(maxiter=250) sampler = Sampler() qaoa = QAOA(sampler=sampler, optimizer=spsa, reps=5) algorithm = MinimumEigenOptimizer(qaoa) result = algorithm.solve(problem) print(result.prettyprint()) # prints solution, x=[1, 0, 1, 0], the cost, fval=4 ``` ### Further examples Learning path notebooks may be found in the [optimization tutorials](https://qiskit.org/documentation/optimization/tutorials/index.html) section %package help Summary: Development documents and examples for qiskit-optimization Provides: python3-qiskit-optimization-doc %description help ### Optional Installs * **IBM CPLEX** may be installed using `pip install 'qiskit-optimization[cplex]'` to enable the reading of `LP` files and the usage of the `CplexOptimizer`, wrapper for ``cplex.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 'qiskit-optimization[cvx]'`. CVXPY being installed will enable the usage of the Goemans-Williamson algorithm as an optimizer `GoemansWilliamsonOptimizer`. * **Matplotlib** may be installed using the command `pip install 'qiskit-optimization[matplotlib]'`. Matplotlib being installed will enable the usage of the `draw` method in the graph optimization application classes. * **Gurobipy** may be installed using the command `pip install 'qiskit-optimization[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 [Max-Cut](https://en.wikipedia.org/wiki/Maximum_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. ```python from docplex.mp.model import Model from qiskit_optimization.algorithms import MinimumEigenOptimizer from qiskit_optimization.translators import from_docplex_mp from qiskit.utils import algorithm_globals from qiskit.primitives import Sampler from qiskit.algorithms.minimum_eigensolvers import QAOA from qiskit.algorithms.optimizers import SPSA # Generate a graph of 4 nodes n = 4 edges = [(0, 1, 1.0), (0, 2, 1.0), (0, 3, 1.0), (1, 2, 1.0), (2, 3, 1.0)] # (node_i, node_j, weight) # Formulate the problem as a Docplex model model = Model() # Create n binary variables x = model.binary_var_list(n) # Define the objective function to be maximized model.maximize(model.sum(w * x[i] * (1 - x[j]) + w * (1 - x[i]) * x[j] for i, j, w in edges)) # Fix node 0 to be 1 to break the symmetry of the max-cut solution model.add(x[0] == 1) # Convert the Docplex model into a `QuadraticProgram` object problem = from_docplex_mp(model) # Run quantum algorithm QAOA on qasm simulator seed = 1234 algorithm_globals.random_seed = seed spsa = SPSA(maxiter=250) sampler = Sampler() qaoa = QAOA(sampler=sampler, optimizer=spsa, reps=5) algorithm = MinimumEigenOptimizer(qaoa) result = algorithm.solve(problem) print(result.prettyprint()) # prints solution, x=[1, 0, 1, 0], the cost, fval=4 ``` ### Further examples Learning path notebooks may be found in the [optimization tutorials](https://qiskit.org/documentation/optimization/tutorials/index.html) section %prep %autosetup -n qiskit-optimization-0.5.0 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-qiskit-optimization -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue May 30 2023 Python_Bot - 0.5.0-1 - Package Spec generated