Qiskit Optimization v0.6 Migration Guide#
This tutorial will guide you through the process of migrating your code from Qiskit Optimization v0.5 to v0.6.
Overview#
Qiskit Terra v0.25 deprecated the qiskit.algorithms module. It has been
superseded by a new standalone library
Qiskit Algorithms.
Qiskit Optimization v0.6 supports only the new algorithms of Qiskit Algorithms.
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 of the Qiskit Algorithms documentation instead.
We can basically use the existing codes by replacing qiskit.algorithms
with qiskit_algorithms.
MinimumEigenOptimizer#
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.*.
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.
The following is the corresponding table.
Former algorithm |
New algorithm |
|---|---|
|
|
|
|
|
|
|
|
NumPyMinimumEigensolver#
Previously
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
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#
Previously
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
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#
Previously
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
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#
Previously
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
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#
Previously
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
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)