Add SBPLX optimizer

.pylintdict

@@ -253,6 +253,7 @@ izaac
 izz
 jac
 jacobian
+johnson
 jm
 jonathan
 jones
@@ -487,6 +488,7 @@ sanjiv
 sashank
 satisfiability
 satyen
+sbplx
 scalability
 schroediger
 schroedinger
@@ -529,13 +531,15 @@ stdout
 stefano
 steppable
 stepsize
+steven
 str
 stratifications
 stratification
 subcircuits
 subclassed
 subclasses
 subcomponents
+subplex
 submodules
 subobjects
 subseteq
@@ -627,4 +631,4 @@ zz
 ω
 φ_i
 φ_ij
-Δ
+Δ

qiskit_machine_learning/optimizers/__init__.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
@@ -32,56 +32,57 @@
 ----------------------
 
 .. autosummary::
-   :toctree: ../stubs/
-   :nosignatures:
+    :toctree: ../stubs/
+    :nosignatures:
 
-   OptimizerResult
-   Optimizer
-   Minimizer
+    OptimizerResult
+    Optimizer
+    Minimizer
 
 Steppable optimization
 ----------------------
 
 .. autosummary::
-   :toctree: ../stubs/
+    :toctree: ../stubs/
 
-   optimizer_utils
+    optimizer_utils
 
 .. autosummary::
-   :toctree: ../stubs/
-   :nosignatures:
+    :toctree: ../stubs/
+    :nosignatures:
 
-   SteppableOptimizer
-   AskData
-   TellData
-   OptimizerState
+    SteppableOptimizer
+    AskData
+    TellData
+    OptimizerState
 
 
 Local optimizers
 ----------------
 
 .. autosummary::
-   :toctree: ../stubs/
-   :nosignatures:
-
-   ADAM
-   AQGD
-   CG
-   COBYLA
-   L_BFGS_B
-   GSLS
-   GradientDescent
-   GradientDescentState
-   NELDER_MEAD
-   NFT
-   P_BFGS
-   POWELL
-   SLSQP
-   SPSA
-   QNSPSA
-   TNC
-   SciPyOptimizer
-   UMDA
+    :toctree: ../stubs/
+    :nosignatures:
+
+    ADAM
+    AQGD
+    CG
+    COBYLA
+    L_BFGS_B
+    GSLS
+    GradientDescent
+    GradientDescentState
+    NELDER_MEAD
+    NFT
+    P_BFGS
+    POWELL
+    SLSQP
+    SPSA
+    QNSPSA
+    TNC
+    SciPyOptimizer
+    UMDA
+
 
 The optimizers from
 `scikit-quant <https://scikit-quant.readthedocs.io/en/latest/>`_ are not included in the
@@ -91,21 +92,32 @@
 https://github.com/qiskit-community/qiskit-algorithms/issues/84.
 
 
+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
 core function and can only be used if the optional dependent ``NLOpt`` package is installed.
 To install the ``NLOpt`` dependent package you can use ``pip install nlopt``.
 
 .. autosummary::
-   :toctree: ../stubs/
-   :nosignatures:
+    :toctree: ../stubs/
+    :nosignatures:
 
-   CRS
-   DIRECT_L
-   DIRECT_L_RAND
-   ESCH
-   ISRES
+    CRS
+    DIRECT_L
+    DIRECT_L_RAND
+    ESCH
+    ISRES
 
 """
 
@@ -123,6 +135,7 @@
 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
@@ -165,5 +178,6 @@
     "DIRECT_L_RAND",
     "ESCH",
     "ISRES",
+    "SBPLX",
     "UMDA",
 ]

qiskit_machine_learning/optimizers/nlopts/__init__.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
@@ -10,11 +10,12 @@
 # copyright notice, and modified files need to carry a notice indicating
 # that they have been altered from the originals.
 
-"""NLopt based global optimizers"""
+"""NLopt-based global and local optimizers"""
 
 from .crs import CRS
 from .direct_l import DIRECT_L
 from .direct_l_rand import DIRECT_L_RAND
 from .esch import ESCH
 from .isres import ISRES
+from .sbplx import SBPLX
 from .nloptimizer import NLoptOptimizer

qiskit_machine_learning/optimizers/nlopts/nloptimizer.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
@@ -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 local and global 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_machine_learning/optimizers/nlopts/sbplx.py

@@ -0,0 +1,36 @@
+# 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.
+    While these claims seem to be true in many cases, we could not find any proof that
+    Subplex is globally convergent, and perhaps it may fail for some objective functions
+    like Nelder-Mead; YMMV.)', 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/add-sbplx-optimizer-d23fb5925879fdae.yaml

@@ -0,0 +1,8 @@
+---
+features:
+  - |
+    Support for :class:`.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 behavior.
+    Further information about this optimizer and the others can be found in
+    the API ref for the :mod:`~qiskit_machine_learning.optimizers`.

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
@@ -39,6 +39,7 @@
     Optimizer,
     P_BFGS,
     POWELL,
+    SBPLX,
     SLSQP,
     SPSA,
     QNSPSA,
@@ -221,6 +222,7 @@ 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):