Adds supports for SBPLX optimizer from nlopt

.pylintdict

@@ -162,6 +162,7 @@ izaac
 izz
 jac
 jacobian
+johnson
 jones
 july
 kandala
@@ -311,6 +312,7 @@ sanjiv
 sashank
 satisfiability
 satyen
+sbplx
 schrödinger
 schroediger
 schroedinger
@@ -337,11 +339,13 @@ stdout
 stefano
 steppable
 stepsize
+steven
 str
 subcircuits
 subclassed
 subclasses
 subcomponents
+subplex
 subspaces
 suzuki
 swappable

qiskit_algorithms/optimizers/__init__.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2018, 2023.
+# (C) Copyright IBM 2018, 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
@@ -98,6 +98,16 @@
    IMFIL
    SNOBFIT
 
+Qiskit also provides local optimizers based on
+`NLOpt <https://nlopt.readthedocs.io/en/latest/>`_.
+See Global Optimizers section below for the optional NLOpt installation instructions.
+
+.. autosummary::
+   :toctree: ../stubs/
+   :nosignatures:
+
+   SBPLX
+
 Global Optimizers
 -----------------
 The global optimizers here all use `NLOpt <https://nlopt.readthedocs.io/en/latest/>`_ for their
@@ -132,6 +142,7 @@
 from .nlopts.direct_l_rand import DIRECT_L_RAND
 from .nlopts.esch import ESCH
 from .nlopts.isres import ISRES
+from .nlopts.sbplx import SBPLX
 from .steppable_optimizer import SteppableOptimizer, AskData, TellData, OptimizerState
 from .optimizer import Minimizer, Optimizer, OptimizerResult, OptimizerSupportLevel
 from .p_bfgs import P_BFGS
@@ -175,6 +186,7 @@
     "DIRECT_L_RAND",
     "ESCH",
     "ISRES",
+    "SBPLX",
     "SNOBFIT",
     "BOBYQA",
     "IMFIL",

qiskit_algorithms/optimizers/nlopts/nloptimizer.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2018, 2023.
+# (C) Copyright IBM 2018, 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
@@ -33,12 +33,13 @@ class NLoptOptimizerType(Enum):
     GN_DIRECT_L = 3
     GN_ESCH = 4
     GN_ISRES = 5
+    LN_SBPLX = 6
 
 
 @_optionals.HAS_NLOPT.require_in_instance
 class NLoptOptimizer(Optimizer):
     """
-    NLopt global optimizer base class
+    NLopt global and local optimizer base class
     """
 
     _OPTIONS = ["max_evals"]
@@ -64,6 +65,7 @@ def __init__(self, max_evals: int = 1000) -> None:  # pylint: disable=unused-arg
             NLoptOptimizerType.GN_DIRECT_L: nlopt.GN_DIRECT_L,
             NLoptOptimizerType.GN_ESCH: nlopt.GN_ESCH,
             NLoptOptimizerType.GN_ISRES: nlopt.GN_ISRES,
+            NLoptOptimizerType.LN_SBPLX: nlopt.LN_SBPLX,
         }
 
     @abstractmethod

qiskit_algorithms/optimizers/nlopts/sbplx.py

@@ -0,0 +1,37 @@
+# This code is part of a Qiskit project.
+#
+# (C) Copyright IBM 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.
+
+"""Sbplx (Subplex) optimizer."""
+
+from .nloptimizer import NLoptOptimizer, NLoptOptimizerType
+
+
+class SBPLX(NLoptOptimizer):
+    """
+    Subplex optimizer.
+
+    "Subplex (a variant of Nelder-Mead that uses Nelder-Mead on a sequence of subspaces)
+    is claimed to be much more efficient and robust than the original Nelder-Mead,
+    while retaining the latter's facility with discontinuous objectives,
+    and in my experience these claims seem to be true in many cases.
+    (However, I'm not aware of any proof that Subplex is globally convergent,
+    and perhaps it may fail for some objectives like Nelder-Mead; YMMV.)"
+    Description by Steven G. Johnson, author of NLopt library.
+
+    NLopt local optimizer, derivative-free.
+    For further detail, please refer to
+    https://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/#sbplx-based-on-subplex
+    """
+
+    def get_nlopt_optimizer(self) -> NLoptOptimizerType:
+        """Return NLopt optimizer type."""
+        return NLoptOptimizerType.LN_SBPLX

releasenotes/notes/sbplx_optimizer-a27361fc4ac8de8c.yaml

@@ -0,0 +1,8 @@
+---
+features:
+  - |
+    Support for SBPLX optimizer from NLopt library has been added.
+    SBPLX is a local gradient-free optimizer based on Nelder-Mead and
+    is expected to show better convergence behaviour.
+    Further information about this and all other optimizers can be found
+    `in the API ref. here <https://qiskit-community.github.io/qiskit-algorithms/apidocs/qiskit_algorithms.optimizers.html>__`.

test/optimizers/test_optimizers.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2018, 2024.
+# (C) Copyright IBM 2018, 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
@@ -42,6 +42,7 @@
     Optimizer,
     P_BFGS,
     POWELL,
+    SBPLX,
     SLSQP,
     SPSA,
     QNSPSA,
@@ -224,6 +225,7 @@ def test_scipy_optimizer_parse_bounds(self):
         (CRS, False),
         (DIRECT_L, False),
         (DIRECT_L_RAND, False),
+        (SBPLX, True),
     )
     @unpack
     def test_nlopt(self, optimizer_cls, use_bound):