Merge branch 'main' into python39

Author: t-imamichi

.mergify.yml

@@ -1,23 +1,23 @@
 queue_rules:
   - name: automerge
-    conditions:
-      - check-success=Deprecation_Messages_and_Coverage (3.8)
-
-pull_request_rules:
-  - name: automatic merge on CI success and review
-    conditions:
+    queue_conditions:
       - check-success=Deprecation_Messages_and_Coverage (3.8)
       - "#approved-reviews-by>=1"
       - label=automerge
       - label!=on hold
-    actions:
-      queue:
-        name: automerge
-        method: squash
+    merge_conditions:
+      - check-success=Deprecation_Messages_and_Coverage (3.8)
+    merge_method: squash
+
+pull_request_rules:
   - name: backport
     conditions:
       - label=stable backport potential
     actions:
       backport:
         branches:
           - stable/0.6
+  - name: automatic merge on CI success and review
+    conditions: []
+    actions:
+      queue:

.pylintdict

@@ -6,7 +6,9 @@ aer's
 al
 annealers
 ansatz
+ansatzes
 apidocs
+apl
 applegate
 args
 arxiv
@@ -15,14 +17,17 @@ autosummary
 backend
 backends
 barkoutsos
+batchsize
 benchmarking
+bfgs
 bitstring
 bitstrings
 bixby
 bool
 boolean
 boyd
 bravyi
+callables
 catol
 cartan
 chu
@@ -35,16 +40,20 @@ conv
 const
 cplex
 cplexoptimizer
+crs
 cts
 currentmodule
 cvar
+dataclass
 deterministically
 dic
 dicts
+dimensionality
 disp
 dmitry
 docplex
 docplex's
+docstring
 doi
 dok
 dtype
@@ -55,22 +64,31 @@ eigen
 eigensolver
 eigensolvers
 eigenstate
+eigenstates
 embeddings
 entangler
 enum
 eq
 et
+eval
+evals
 exponentiated
+failsafe
 farhi
 fmin
 formatter
+frac
 func
+functools
 fred
 fval
 fx
+f'spsa
 gambella
 geq
 getter
+getters
+globals
 glover
 goemans
 goldstone
@@ -81,11 +99,13 @@ gurobi
 gurobioptimizer
 gurobipy
 gutmann
+hadfield
 hamilton
 hamiltonian
 hamiltonians
 hastings
 hayashi
+hessians
 hoyer
 https
 ibm
@@ -99,7 +119,9 @@ iprint
 ising
 iter
 iteratively
+jac
 july
+kandala
 karimi
 kirkpatrick
 kwargs
@@ -108,22 +130,28 @@ len
 leq
 lhs
 lin
+linalg
 linearconstraint
 linexpr
+linter
 lowerbound
 lp
+lse
 lucas
 macos
 makefile
+marecek
 masahito
 matplotlib
 maxcut
+maxfev
 maxfun
 maxiter
 mdl
 milp
 minimizer
 minimumeigenoptimizer
+modelspace
 mmp
 mpm
 multiset
@@ -132,9 +160,13 @@ nannicini
 natively
 ndarray
 ndarrays
+nones
 noop
+nelder
 networkx
 neven
+nfev
+nft
 nosignatures
 np
 num
@@ -147,18 +179,23 @@ optimality
 optimizationresult
 optimizationresultstatus
 optimizers
+packagebut
 panchenko
 param
+parameterizations
 params
 parikh
+passmanager
 pauli
 paulis
 peleato
 pmm
+polyfit
 pooya
 pos
 ppp
 pre
+preconditioner
 preprint
 prepend
 presolver
@@ -187,6 +224,8 @@ qubo
 readme
 repr
 representable
+resamplings
+rescaling
 rhobeg
 rhoend
 rhs
@@ -196,22 +235,30 @@ robert
 ronagh
 rtype
 runtime
+rustworkx
 ry
 rz
 sahar
 scipy
 sdp
+serializable
 sherrington
 simonetto
 slsqp
 smode
 smoothen
+spall
+spedalieri
 spsa
 src
 statevector
 stdout
 stephen
+steppable
+stepsize
 str
+subclassed
+subclasses
 subcollection
 subgraph
 submodules
@@ -222,6 +269,7 @@ summands
 tavernelli
 terra
 th
+tnc
 toctree
 todok
 tol
@@ -241,13 +289,17 @@ variational
 vartype
 vqe
 vqeresult
+utils
 writelines
+xatol
 xixj
+xopt
 wavefunction
 wecker
 whitespace
 wiesner
 williamson
+woerner
 xs
 ys
 zemlin

README.md

@@ -70,13 +70,12 @@ from docplex.mp.model import Model
 
 from qiskit_optimization.algorithms import MinimumEigenOptimizer
 from qiskit_optimization.translators import from_docplex_mp
+from qiskit_optimization.utils import algorithm_globals
+from qiskit_optimization.minimum_eigensolvers import QAOA
+from qiskit_optimization.optimizers import SPSA
 
 from qiskit.primitives import Sampler
 
-from qiskit_algorithms.utils import algorithm_globals
-from qiskit_algorithms 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)

docs/tutorials/06_examples_max_cut_and_tsp.ipynb

@@ -527,11 +527,11 @@
    "source": [
     "## Traveling Salesman Problem\n",
     "\n",
-    "In addition to being a notorious NP-complete problem that has drawn the attention of computer scientists and mathematicians for over two centuries, the Traveling Salesman Problem (TSP) has important bearings on finance and marketing, as its name suggests. Colloquially speaking, the traveling salesman is a person that goes from city to city to sell merchandise. The objective in this case is to find the shortest path that would enable the salesman to visit all the cities and return to its hometown, i.e. the city where he started traveling. By doing this, the salesman gets to maximize potential sales in the least amount of time. \n",
+    "In addition to being a notorious NP-complete problem that has drawn the attention of computer scientists and mathematicians for over two centuries, the Traveling Salesman Problem (TSP) has important bearings on finance and marketing, as its name suggests. Colloquially speaking, the traveling salesman is a person that goes from city to city to sell merchandise. The objective in this case is to find the shortest path that would enable the salesman to visit all the cities and return to his hometown, i.e. the city where he started traveling. By doing this, the salesman gets to maximize potential sales in the least amount of time. \n",
     "\n",
     "The problem derives its importance from its \"hardness\" and ubiquitous equivalence to other relevant combinatorial optimization problems that arise in practice.\n",
     " \n",
-    "The mathematical formulation with some early analysis was proposed by W.R. Hamilton in the early 19th century. Mathematically the problem is, as in the case of Max-Cut, best abstracted in terms of graphs. The TSP on the nodes of a graph asks for the shortest *Hamiltonian cycle* that can be taken through each of the nodes. A Hamilton cycle is a closed path that uses every vertex of a graph once. The general solution is unknown and an algorithm that finds it efficiently (e.g., in polynomial time) is not expected to exist.\n",
+    "The mathematical formulation with some early analysis was proposed by W.R. Hamilton in the early 19th century. Mathematically the problem is, as in the case of Max-Cut, best abstracted in terms of graphs. The TSP on the nodes of a graph asks for the shortest *Hamiltonian cycle* that can be taken through each of the nodes. A Hamiltonian cycle is a closed path that uses every vertex of a graph once. The general solution is unknown and an algorithm that finds it efficiently (e.g., in polynomial time) is not expected to exist.\n",
     "\n",
     "Find the shortest Hamiltonian cycle in a graph $G=(V,E)$ with $n=|V|$ nodes and distances, $w_{ij}$ (distance from vertex $i$ to vertex $j$). A Hamiltonian cycle is described by $N^2$ variables $x_{i,p}$, where $i$ represents the node and $p$ represents its order in a prospective cycle. The decision variable takes the value 1 if the solution occurs at node $i$ at time order $p$. We require that every node can only appear once in the cycle, and for each time a node has to occur. This amounts to the two constraints (here and in the following, whenever not specified, the summands run over 0,1,...N-1)\n",
     "\n",

qiskit_optimization/__init__.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2019, 2024.
+# (C) Copyright IBM 2019, 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
@@ -85,9 +85,15 @@
 
 """
 
-from .exceptions import QiskitOptimizationError
+from .exceptions import QiskitOptimizationError, AlgorithmError
 from .infinity import INFINITY  # must be at the top of the file
 from .problems.quadratic_program import QuadraticProgram
 from .version import __version__
 
-__all__ = ["__version__", "QuadraticProgram", "QiskitOptimizationError", "INFINITY"]
+__all__ = [
+    "__version__",
+    "QuadraticProgram",
+    "QiskitOptimizationError",
+    "AlgorithmError",
+    "INFINITY",
+]

qiskit_optimization/algorithm_job.py

@@ -0,0 +1,45 @@
+# This code is part of a Qiskit project.
+#
+# (C) Copyright IBM 2022, 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
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+"""
+AlgorithmJob class
+"""
+from qiskit.primitives.primitive_job import PrimitiveJob
+
+
+class AlgorithmJob(PrimitiveJob):
+    """
+    This class is introduced for typing purposes and provides no
+    additional function beyond that inherited from its parents.
+
+    Update: :meth:`AlgorithmJob.submit()` method added. See its
+    documentation for more info.
+    """
+
+    def submit(self) -> None:
+        """
+        Submit the job for execution.
+
+        For V1 primitives, Qiskit ``PrimitiveJob`` subclassed JobV1 and defined ``submit()``.
+        ``PrimitiveJob`` was updated for V2 primitives, no longer subclasses ``JobV1``, and
+        now has a private ``_submit()`` method, with ``submit()`` being deprecated as of
+        Qiskit version 0.46. This maintains the ``submit()`` for ``AlgorithmJob`` here as
+        it's called in many places for such a job. An alternative could be to make
+        0.46 the required minimum version and alter all algorithm's call sites to use
+        ``_submit()`` and make this an empty class again as it once was. For now this
+        way maintains compatibility with the current min version of 0.44.
+        """
+        # TODO: Considering changing this in the future - see above docstring.
+        try:
+            super()._submit()
+        except AttributeError:
+            super().submit()  # pylint: disable=no-member