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superseded by a new standalone library | ||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