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Add Prelude 0.6 (#566)
* add prelude of v0.6 * add migration guide to v0.6 * update * Update releasenotes/notes/switch-to-qiskit-algorithms-b0af38fe547bf5b5.yaml --------- Co-authored-by: Steve Wood <[email protected]>
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‎docs/migration/01_migration_guide_to_v0.5.rst

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The following is the corresponding table.
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+-------------------------------------+--------------------------------+
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| Former algorithm | New algorithm |
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+=====================================+================================+
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| ``qis | ``qiskit. |
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| kit.algorithms.MinimumEigenSolver`` | algorithms.minimum_eigensolver |
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| | s.SamplingMinimumEigensolver`` |
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+-------------------------------------+--------------------------------+
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| ``qiskit.a | ``qis |
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| lgorithms.NumPyMinimumEigensolver`` | kit.algorithms.minimum_eigenso |
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| | lver.NumPyMinimumEigensolver`` |
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+-------------------------------------+--------------------------------+
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| ``qiskit.algorithms.QAOA`` | ``qiskit.algorith |
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| | ms.minimum_eigensolvers.QAOA`` |
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+-------------------------------------+--------------------------------+
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| ``qiskit.algorithms.VQE`` | ``qiskit.algorithms.mini |
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| | mum_eigensolvers.SamplingVQE`` |
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+-------------------------------------+--------------------------------+
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.. csv-table::
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:header: Former algorithm, New algorithm
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``qiskit.algorithms.MinimumEigenSolver``, ``qiskit.algorithms.minimum_eigensolvers.SamplingMinimumEigensolver``
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``qiskit.algorithms.NumPyMinimumEigensolver``, ``qiskit.algorithms.minimum_eigensolver.NumPyMinimumEigensolver``
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``qiskit.algorithms.QAOA``, ``qiskit.algorithms.minimum_eigensolvers.QAOA``
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``qiskit.algorithms.VQE``, ``qiskit.algorithms.minimum_eigensolvers.SamplingVQE``
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Setup of a problem
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‎docs/migration/02_migration_guide_to_v0.6.rst

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Qiskit Optimization v0.6 Migration Guide
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========================================
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This tutorial will guide you through the process of migrating your code
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from Qiskit Optimization v0.5 to v0.6.
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Overview
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--------
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Qiskit Terra v0.25 deprecated the ``qiskit.algorithms`` module. It has been
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superseded by a new standalone library
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`Qiskit Algorithms <https://github.com/qiskit-community/qiskit_algorithms>`__.
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Qiskit Optimization v0.6 supports only the new algorithms of Qiskit Algorithms.
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It is not the intention to provide detailed explanations of the
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new Qiskit Algorithms in this migration guide. We suggest that you read the
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`corresponding
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resources <https://qiskit.org/ecosystem/algorithms/index.html>`__
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of the Qiskit Algorithms documentation instead.
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We can basically use the existing codes by replacing ``qiskit.algorithms``
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with ``qiskit_algorithms``.
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``MinimumEigenOptimizer``
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-------------------------
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The former algorithms exist in
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``qiskit.algorithms.minimum_eigensolvers``.
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On the other hand, the new algorithms exist in
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``qiskit_algorithms.minimum_eigensolvers`` and we can access them by
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``qiskit_algorithms.*``.
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``MinimumEigenOptimizer`` of Qiskit Optimization can use
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``qiskit_algorithms.SamplingMinimumEigensolver``
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interface of the new algorithms. Note that ``MinimumEigenOptimizer``
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cannot basically handle
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``qiskit_algorithms.MinimumEigensolver`` of the new
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algorithms. But there is an exception. ``MinimumEigenOptimizer`` can
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handle ``qiskit_algorithms.NumPyMinimumEigensolver``
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because ``qiskit_algorithms.NumPyMinimumEigensolver`` has
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an extension that allows users to access the eigen states.
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The following is the corresponding table.
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.. csv-table::
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:header: Former algorithm, New algorithm
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``qiskit.algorithms.minimum_eigensolvers.SamplingMinimumEigensolver``, ``qiskit_algorithms.SamplingMinimumEigensolver``
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``qiskit.algorithms.minimum_eigensolver.NumPyMinimumEigensolver``, ``qiskit_algorithms.NumPyMinimumEigensolver``
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``qiskit.algorithms.minimum_eigensolvers.QAOA``, ``qiskit_algorithms.QAOA``
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``qiskit.algorithms.minimum_eigensolvers.SamplingVQE``, ``qiskit_algorithms.SamplingVQE``
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NumPyMinimumEigensolver
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~~~~~~~~~~~~~~~~~~~~~~~
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Previously
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.. code:: python
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from qiskit.algorithms.minimum_eigensolvers import NumPyMinimumEigensolver
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from qiskit_optimization.algorithms import MinimumEigenOptimizer
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mes = NumPyMinimumEigensolver()
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meo = MinimumEigenOptimizer(min_eigen_solver=mes)
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result = meo.solve(problem)
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print(result)
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New
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.. code:: python
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from qiskit_algorithms import NumPyMinimumEigensolver
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from qiskit_optimization.algorithms import MinimumEigenOptimizer
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mes = NumPyMinimumEigensolver()
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meo = MinimumEigenOptimizer(min_eigen_solver=mes)
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result = meo.solve(problem)
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print(result)
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QAOA
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~~~~
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Previously
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.. code:: python
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from qiskit.algorithms.minimum_eigensolvers import QAOA
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from qiskit.algorithms.optimizers import COBYLA
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from qiskit.primitives import Sampler
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from qiskit_optimization.algorithms import MinimumEigenOptimizer
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shots = 1000
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mes = QAOA(sampler=Sampler(), optimizer=COBYLA())
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meo = MinimumEigenOptimizer(min_eigen_solver=mes)
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result = meo.solve(problem)
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print(result)
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New
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.. code:: python
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from qiskit_algorithms import QAOA
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from qiskit_algorithms.optimizers import COBYLA
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from qiskit.primitives import Sampler
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from qiskit_optimization.algorithms import MinimumEigenOptimizer
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shots = 1000
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mes = QAOA(sampler=Sampler(), optimizer=COBYLA())
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meo = MinimumEigenOptimizer(min_eigen_solver=mes)
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result = meo.solve(problem)
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print(result)
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SamplingVQE
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~~~~~~~~~~~
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Previously
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.. code:: python
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from qiskit.algorithms.minimum_eigensolvers import SamplingVQE
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from qiskit.algorithms.optimizers import COBYLA
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from qiskit.circuit.library import RealAmplitudes
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from qiskit.primitives import Sampler
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from qiskit_optimization.algorithms import MinimumEigenOptimizer
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mes = SamplingVQE(sampler=Sampler(), ansatz=RealAmplitudes(), optimizer=COBYLA())
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meo = MinimumEigenOptimizer(min_eigen_solver=mes)
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result = meo.solve(problem)
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print(result)
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.. code:: python
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from qiskit_algorithms import SamplingVQE
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from qiskit_algorithms.optimizers import COBYLA
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from qiskit.circuit.library import RealAmplitudes
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from qiskit.primitives import Sampler
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from qiskit_optimization.algorithms import MinimumEigenOptimizer
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mes = SamplingVQE(sampler=Sampler(), ansatz=RealAmplitudes(), optimizer=COBYLA())
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meo = MinimumEigenOptimizer(min_eigen_solver=mes)
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result = meo.solve(problem)
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print(result)
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``WarmStartQAOAOptimizer``
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--------------------------
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.. code:: python
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from qiskit.algorithms.minimum_eigensolvers import QAOA
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from qiskit.algorithms.optimizers import COBYLA
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from qiskit.primitives import Sampler
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from qiskit_optimization.algorithms import WarmStartQAOAOptimizer, SlsqpOptimizer
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qaoa = QAOA(sampler=Sampler(), optimizer=COBYLA())
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optimizer = WarmStartQAOAOptimizer(
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pre_solver=SlsqpOptimizer(), relax_for_pre_solver=True, qaoa=qaoa, epsilon=0.25
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)
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result = optimizer.solve(problem)
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print(result)
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New
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.. code:: python
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from qiskit_algorithms import QAOA
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from qiskit_algorithms.optimizers import COBYLA
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from qiskit.primitives import Sampler
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from qiskit_optimization.algorithms import WarmStartQAOAOptimizer, SlsqpOptimizer
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qaoa = QAOA(sampler=Sampler(), optimizer=COBYLA())
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optimizer = WarmStartQAOAOptimizer(
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pre_solver=SlsqpOptimizer(), relax_for_pre_solver=True, qaoa=qaoa, epsilon=0.25
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)
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result = optimizer.solve(problem)
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print(result)
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``GroverOptimizer``
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-------------------
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.. code:: python
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from qiskit.algorithms.optimizers import COBYLA
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from qiskit.primitives import Sampler
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from qiskit_optimization.algorithms import GroverOptimizer
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optimizer = GroverOptimizer(num_value_qubits=3, num_iterations=3, sampler=Sampler())
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result = optimizer.solve(problem)
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print(result)
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New
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.. code:: python
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from qiskit_algorithms.optimizers import COBYLA
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from qiskit.primitives import Sampler
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from qiskit_optimization.algorithms import GroverOptimizer
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optimizer = GroverOptimizer(num_value_qubits=3, num_iterations=3, sampler=Sampler())
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result = optimizer.solve(problem)
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print(result)

‎docs/migration/index.rst

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Qiskit Optimization Migration Guide
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###################################
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If you are switching over to version 0.6, which has removed all previously
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deprecated code, simply follow the instructions provided in the sections below.
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.. nbgallery::
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:glob:

‎releasenotes/notes/0.6-prelude-2740977b31d22583.yaml

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---
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prelude: >
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Qiskit Optimization has been migrated to the `qiskit-community Github organization <https://github.com/qiskit-community>`_
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to further emphasize that it is a community-driven project.
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To reflect this change and because we are on-boarding additional code owners and maintainers,
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with this version (0.6) we have decided to *remove all deprecated* code, regardless of the time of its deprecation.
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This ensures that the new members of the development team do not have a large bulk of legacy code to maintain.
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This can mean one of two things for you as the end-user:
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#. Nothing, if you already migrated your code and no longer rely on any deprecated features.
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#. Otherwise, you need to migrate your code immediately. If you cannot do that, or want to continue using some
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of the features that were removed, you should pin your version of Qiskit Optimization to 0.5
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You can check out the `migration guides <https://qiskit.org/ecosystem/optimization/migration/index.html>`_
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for details on how to update your code.
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For more context on the changes around Qiskit Optimization and the other application projects as well as the algorithms
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library in Qiskit, be sure to read this `blog post <https://ibm.biz/BdSyNm>`_.

‎releasenotes/notes/switch-to-qiskit-algorithms-b0af38fe547bf5b5.yaml

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---
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prelude: >
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Qiskit Optimization 0.6 switches from ``qiskit.algorithms`` of Qiskit Terra
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Qiskit Optimization 0.6 switches from ``qiskit.algorithms`` of Qiskit
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to `Qiskit Algorithms <https://qiskit.org/ecosystem/algorithms/>`_.
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Qiskit Optimization 0.6 drops supports of the former algorithms based on
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``qiskit.algorithms``, ``QuantumInstance``, and Opflow of Qiskit Terra.
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``qiskit.algorithms``, ``QuantumInstance``, and Opflow of Qiskit.
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upgrade:
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- |

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