[WIP] Remove qiskit-algorithms dependency

.pylintdict

@@ -10,6 +10,7 @@ ansatzes
 apidocs
 apl
 applegate
+arg
 args
 arxiv
 atol
@@ -26,18 +27,22 @@ bixby
 bool
 boolean
 boyd
+brassard
 bravyi
+byteorder
 callables
-catol
 cartan
+catol
 chu
+chuang
 chvátal
+clbits
 cobyla
 coeff
 coeffs
 combinatorial
-conv
 const
+conv
 cplex
 cplexoptimizer
 crs
@@ -73,17 +78,17 @@ et
 eval
 evals
 exponentiated
+f'spsa
 failsafe
 farhi
 fmin
 formatter
 frac
+fred
 func
 functools
-fred
 fval
 fx
-f'spsa
 gambella
 geq
 getter
@@ -99,6 +104,7 @@ gurobi
 gurobioptimizer
 gurobipy
 gutmann
+hadamard
 hadfield
 hamilton
 hamiltonian
@@ -124,8 +130,10 @@ july
 kandala
 karimi
 kirkpatrick
+kwarg
 kwargs
 lagrangian
+langle
 len
 leq
 lhs
@@ -142,6 +150,7 @@ macos
 makefile
 marecek
 masahito
+mathcal
 matplotlib
 maxcut
 maxfev
@@ -151,22 +160,23 @@ mdl
 milp
 minimizer
 minimumeigenoptimizer
-modelspace
 mmp
+modelspace
+mosca
 mpm
 multiset
 mypy
 nannicini
 natively
 ndarray
 ndarrays
-nones
-noop
 nelder
 networkx
 neven
 nfev
 nft
+nones
+noop
 nosignatures
 np
 num
@@ -179,6 +189,7 @@ optimality
 optimizationresult
 optimizationresultstatus
 optimizers
+otimes
 packagebut
 panchenko
 param
@@ -189,21 +200,22 @@ passmanager
 pauli
 paulis
 peleato
+peruzzo
 pmm
 polyfit
 pooya
 pos
 ppp
 pre
 preconditioner
-preprint
 prepend
+preprint
 presolver
 prettyprint
 princeton
 probabilistically
-py
 pxd
+py
 qaoa
 qasm
 qiskit
@@ -221,6 +233,7 @@ quantuminstance
 qubit
 qubits
 qubo
+rangle
 readme
 repr
 representable
@@ -262,10 +275,11 @@ subclasses
 subcollection
 subgraph
 submodules
-subspaces
-sys
 subproblem
+subspaces
 summands
+sys
+tapp
 tavernelli
 terra
 th
@@ -285,21 +299,22 @@ troyer
 tsplib
 undirected
 upperbound
+utils
 variational
 vartype
+vec
 vqe
 vqeresult
-utils
-writelines
-xatol
-xixj
-xopt
 wavefunction
 wecker
 whitespace
 wiesner
 williamson
 woerner
+writelines
+xatol
+xixj
+xopt
 xs
 ys
 zemlin

README.md

@@ -16,8 +16,7 @@ The Optimization module enables easy, efficient modeling of optimization problem
 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
-[Qiskit Algorithms](https://qiskit-community.github.io/qiskit-algorithms/). Furthermore, the modular design
+GroverOptimizer, leveraging fundamental algorithms. 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.

qiskit_optimization/__init__.py

@@ -26,13 +26,10 @@
 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
-(:class:`~qiskit_algorithms.QAOA`), to
+(:class:`~qiskit_optimization.minimum_eigensolvers.QAOA`), to
 `Grover Adaptive Search <https://arxiv.org/abs/quant-ph/9607014>`_
-(:class:`~algorithms.GroverOptimizer`), leveraging
-fundamental `minimum eigensolvers
-<https://qiskit-community.github.io/qiskit-algorithms/apidocs/qiskit_algorithms.html#minimum-eigensolvers>`_
-provided by
-`Qiskit Algorithms <https://qiskit-community.github.io/qiskit-algorithms/>`_.
+(:class:`~qiskit_optimization.algorithms.GroverOptimizer`), leveraging fundamental minimum
+eigensolvers (:class:`~qiskit_optimization.minimum_eigensolvers.MinimumEigensolver`).
 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,
@@ -82,10 +79,15 @@
    converters
    problems
    translators
+   # modules copied from qiskit_algorithms
+   eigensolvers
+   minimum_eigensolvers
+   optimizers
+   utils
 
 """
 
-from .exceptions import QiskitOptimizationError, AlgorithmError
+from .exceptions import AlgorithmError, QiskitOptimizationError
 from .infinity import INFINITY  # must be at the top of the file
 from .problems.quadratic_program import QuadraticProgram
 from .version import __version__

qiskit_optimization/algorithms/admm_optimizer.py

@@ -17,9 +17,9 @@
 from typing import List, Optional, Tuple, cast
 
 import numpy as np
-from qiskit_algorithms import NumPyMinimumEigensolver
 
 from ..converters import MaximizeToMinimize
+from ..minimum_eigensolvers import NumPyMinimumEigensolver
 from ..problems.constraint import Constraint
 from ..problems.linear_constraint import LinearConstraint
 from ..problems.linear_expression import LinearExpression

qiskit_optimization/algorithms/grover_optimizer.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2020, 2024.
+# (C) Copyright IBM 2020, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -21,19 +21,18 @@
 from qiskit import QuantumCircuit, QuantumRegister
 from qiskit.circuit.library import QuadraticForm
 from qiskit.primitives import BaseSampler
-from qiskit_algorithms import AmplificationProblem
-from qiskit_algorithms.amplitude_amplifiers.grover import Grover
-from qiskit_algorithms.utils import algorithm_globals
 
 from qiskit_optimization.algorithms.optimization_algorithm import (
     OptimizationAlgorithm,
     OptimizationResult,
     OptimizationResultStatus,
     SolutionSample,
 )
+from qiskit_optimization.amplitude_amplifiers.grover import AmplificationProblem, Grover
 from qiskit_optimization.converters import QuadraticProgramConverter, QuadraticProgramToQubo
 from qiskit_optimization.exceptions import QiskitOptimizationError
 from qiskit_optimization.problems import QuadraticProgram, Variable
+from qiskit_optimization.utils import algorithm_globals
 
 logger = logging.getLogger(__name__)
 
@@ -191,7 +190,7 @@ def solve(self, problem: QuadraticProgram) -> OptimizationResult:
             while not improvement_found:
                 # Determine the number of rotations.
                 loops_with_no_improvement += 1
-                rotation_count = algorithm_globals.random.integers(0, m)
+                rotation_count = int(algorithm_globals.random.integers(0, m))
                 rotations += rotation_count
                 # Apply Grover's Algorithm to find values below the threshold.
                 # TODO: Utilize Grover's incremental feature - requires changes to Grover.

qiskit_optimization/algorithms/minimum_eigen_optimizer.py

@@ -15,15 +15,15 @@
 
 import numpy as np
 from qiskit.quantum_info import SparsePauliOp
+
+from ..converters.quadratic_program_to_qubo import QuadraticProgramConverter, QuadraticProgramToQubo
+from ..exceptions import QiskitOptimizationError
 from ..minimum_eigensolvers import (
     NumPyMinimumEigensolver,
     NumPyMinimumEigensolverResult,
     SamplingMinimumEigensolver,
     SamplingMinimumEigensolverResult,
 )
-
-from ..converters.quadratic_program_to_qubo import QuadraticProgramConverter, QuadraticProgramToQubo
-from ..exceptions import QiskitOptimizationError
 from ..problems.quadratic_program import QuadraticProgram, Variable
 from .optimization_algorithm import (
     OptimizationAlgorithm,
@@ -109,7 +109,7 @@ class MinimumEigenOptimizer(OptimizationAlgorithm):
 
     .. code-block::
 
-        from qiskit_algorithms import QAOA
+        from qiskit_optimization.minimum_eigensolvers import QAOA
         from qiskit_optimization.problems import QuadraticProgram
         from qiskit_optimization.algorithms import MinimumEigenOptimizer
         problem = QuadraticProgram()
@@ -222,7 +222,8 @@ def _solve_internal(
                 raise QiskitOptimizationError(
                     "MinimumEigenOptimizer does not support this minimum eigensolver "
                     f"{type(self._min_eigen_solver)}. "
-                    "You can use qiskit_algorithms.SamplingMinimumEigensolver instead."
+                    "You can use qiskit_optimization.minimum_eigensolvers."
+                    "SamplingMinimumEigensolver instead."
                 )
             if eigen_result.eigenstate is not None:
                 raw_samples = self._eigenvector_to_solutions(

qiskit_optimization/algorithms/qrao/__init__.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2023.
+# (C) Copyright IBM 2023, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -48,8 +48,8 @@
 
 .. code-block:: python
 
-    from qiskit_algorithms.optimizers import COBYLA
-    from qiskit_algorithms import VQE
+    from qiskit_optimization.optimizers import COBYLA
+    from qiskit_optimization.minimum_eigensolvers import VQE
     from qiskit.circuit.library import RealAmplitudes
     from qiskit.primitives import Estimator
 

qiskit_optimization/algorithms/qrao/magic_rounding.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2023, 2024.
+# (C) Copyright IBM 2023, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -19,10 +19,9 @@
 from qiskit import QuantumCircuit
 from qiskit.primitives import BaseSampler
 from qiskit.quantum_info import SparsePauliOp
-from qiskit_algorithms.exceptions import AlgorithmError
 
 from qiskit_optimization.algorithms import OptimizationResultStatus, SolutionSample
-from qiskit_optimization.exceptions import QiskitOptimizationError
+from qiskit_optimization.exceptions import AlgorithmError, QiskitOptimizationError
 
 from .quantum_random_access_encoding import (
     _z_to_21p_qrac_basis_circuit,