Update serialising methods and instructions

.pylintdict

@@ -109,6 +109,7 @@ deterministically
 diag
 dicts
 diederik
+dill
 dimensionality
 dir
 discretization
@@ -500,6 +501,7 @@ sdg
 seealso
 semidefinite
 serializable
+serializablemodelmixin
 shalev
 shanno
 shende

qiskit_machine_learning/algorithms/classifiers/qsvc.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2021, 2024.
+# (C) Copyright IBM 2021, 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
@@ -34,13 +34,24 @@ class QSVC(SVC, SerializableModelMixin):
     Read more in the `scikit-learn user guide
     <https://scikit-learn.org/stable/modules/svm.html#svm-classification>`_.
 
-    **Example**
+    Examples:
+        .. code-block::
 
-    .. code-block::
+            from qiskit_machine_learning.kernels import FidelityQuantumKernel
+            from qiskit_machine_learning.algorithms import QSVC
+
+            kernel = FidelityQuantumKernel()
+            qsvc = QSVC(quantum_kernel=kernel)
+            qsvc.fit(X_train, y_train)
+            y_pred = qsvc.predict(X_test)
+
+            # Save the trained model
+            qsvc.to_dill('qsvc_model.dill')
+
+            # Load the model for later use
+            loaded_qsvc = QSVC.from_dill('qsvc_model.dill')
+            score = loaded_qsvc.score(X_test, y_test)
 
-        qsvc = QSVC(quantum_kernel=qkernel)
-        qsvc.fit(sample_train,label_train)
-        qsvc.predict(sample_test)
     """
 
     def __init__(self, *, quantum_kernel: Optional[BaseKernel] = None, **kwargs):

qiskit_machine_learning/algorithms/serializable_model.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2021, 2023.
+# (C) Copyright IBM 2021, 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
@@ -15,37 +15,70 @@
 
 import dill
 
+from ..utils.deprecation import issue_deprecation_msg
+
 
 class SerializableModelMixin:
     """
-    Provides convenient methods for saving and loading models.
+    Provides convenient methods for saving and loading models via dill serialization.
+
+    .. warning::
+        The legacy :meth:`save` and :meth:`load` methods are deprecated in v0.9.0
+        and will be removed in a future release. Please use :meth:`to_dill`
+        and :meth:`from_dill` respectively.
+
     """
 
-    def save(self, file_name: str) -> None:
+    def to_dill(self, file_name: str) -> None:
         """
         Saves this model to the specified file. Internally, the model is serialized via ``dill``.
         All parameters are saved, including a primitive instance that is referenced by internal
         objects. That means if a model is loaded from a file and is used, for instance, for
         inference, the same primitive will be used even if a cloud primitive was used.
 
+        .. warning::
+            Replaces the deprecated :meth:`save` method.
+
         Args:
-            file_name: a file name or path where to save the model.
+            file_name: Path where the serialized model will be written.
+
+        Example:
+            .. code-block::
+
+                model.to_dill('model_state.dill')
         """
         with open(file_name, "wb") as handler:
             dill.dump(self, handler)
 
+    def save(self, *args) -> None:
+        """Backwards compatibility with :meth:`to_dill`, deprecated in v0.9.0."""
+        issue_deprecation_msg(
+            msg="SerializableModelMixin.save() is deprecated.",
+            version="0.9.0",
+            remedy="Use the to_dill() method instead.",
+            period="4 months",
+        )
+        self.to_dill(*args)
+
     @classmethod
-    def load(cls, file_name: str) -> Any:
+    def from_dill(cls, file_name: str) -> Any:
         """
-        Loads a model from the file. If the loaded model is not an instance of the class whose
+        Loads a model from a file. If the loaded model is not an instance of the class whose
         method was called, then a warning is raised. Nevertheless, the loaded model may be a valid
         model.
 
+        Replaces the deprecated :meth:`load` method.
+
         Args:
-            file_name: a file name or path to load a model from.
+            file_name: Path to the dill file containing the serialized model.
 
         Returns:
-            A loaded model.
+            An instance of the model loaded from disk.
+
+        Example:
+            .. code-block::
+
+                loaded = MyModel.from_dill('model_state.dill')
 
         Raises:
             TypeError: if a loaded model is not an instance of the expected class.
@@ -55,3 +88,14 @@ def load(cls, file_name: str) -> Any:
         if not isinstance(model, cls):
             raise TypeError(f"Loaded model is of class {type(model)}. Expected class: {cls}.")
         return model
+
+    @classmethod
+    def load(cls, *args) -> Any:
+        """Backwards compatibility with :meth:`from_dill`, deprecated in v0.9.0."""
+        issue_deprecation_msg(
+            msg="SerializableModelMixin.load() is deprecated.",
+            version="0.9.0",
+            remedy="Use the from_dill() classmethod instead.",
+            period="4 months",
+        )
+        return cls.from_dill(*args)

releasenotes/notes/serializable-model-update-io-b708094e41edeb35.yaml

@@ -0,0 +1,10 @@
+---
+upgrade:
+  - |
+    Added new methods :meth:`to_dill` and :meth:`from_dill`, with improved docstrings and examples.
+    Deprecated :meth:`save` and :meth:`load` in favor of :meth:`to_dill` and :meth:`from_dill`.
+deprecations:
+  - |
+    The methods :meth:`SerializableModelMixin.save` and :meth:`SerializableModelMixin.load` have
+    been deprecated and will be removed (target v0.9.0). Please use
+    :meth:`SerializableModelMixin.to_dill` and :meth:`SerializableModelMixin.from_dill` instead.

test/algorithms/classifiers/test_fidelity_quantum_kernel_pegasos_qsvc.py

@@ -208,9 +208,9 @@ def test_save_load(self):
 
         # save/load, change the quantum instance and check if predicted values are the same
         file_name = os.path.join(tempfile.gettempdir(), "pegasos.model")
-        regressor.save(file_name)
+        regressor.to_dill(file_name)
         try:
-            regressor_load = PegasosQSVC.load(file_name)
+            regressor_load = PegasosQSVC.from_dill(file_name)
             loaded_model_predicts = regressor_load.predict(test_features)
 
             np.testing.assert_array_almost_equal(original_predicts, loaded_model_predicts)
@@ -222,7 +222,7 @@ class FakeModel(SerializableModelMixin):
                 pass
 
             with self.assertRaises(TypeError):
-                FakeModel.load(file_name)
+                FakeModel.from_dill(file_name)
 
         finally:
             os.remove(file_name)

test/algorithms/classifiers/test_fidelity_quantum_kernel_qsvc.py

@@ -105,9 +105,9 @@ def test_save_load(self):
 
         # save/load, change the quantum instance and check if predicted values are the same
         file_name = os.path.join(tempfile.gettempdir(), "qsvc.model")
-        classifier.save(file_name)
+        classifier.to_dill(file_name)
         try:
-            classifier_load = QSVC.load(file_name)
+            classifier_load = QSVC.from_dill(file_name)
             loaded_model_predicts = classifier_load.predict(test_features)
 
             np.testing.assert_array_almost_equal(original_predicts, loaded_model_predicts)
@@ -119,7 +119,7 @@ class FakeModel(SerializableModelMixin):
                 pass
 
             with self.assertRaises(TypeError):
-                FakeModel.load(file_name)
+                FakeModel.from_dill(file_name)
 
         finally:
             os.remove(file_name)

test/algorithms/classifiers/test_neural_network_classifier.py

@@ -390,9 +390,9 @@ def test_save_load(self, qnn_type):
         # save/load, change the quantum instance and check if predicted values are the same
         with tempfile.TemporaryDirectory() as dir_name:
             file_name = os.path.join(dir_name, "classifier.model")
-            classifier.save(file_name)
+            classifier.to_dill(file_name)
 
-            classifier_load = NeuralNetworkClassifier.load(file_name)
+            classifier_load = NeuralNetworkClassifier.from_dill(file_name)
             loaded_model_predicts = classifier_load.predict(test_features)
 
             np.testing.assert_array_almost_equal(original_predicts, loaded_model_predicts)
@@ -404,7 +404,7 @@ class FakeModel(SerializableModelMixin):
                 pass
 
             with self.assertRaises(TypeError):
-                FakeModel.load(file_name)
+                FakeModel.from_dill(file_name)
 
     @idata((True, False))
     def test_num_classes_data(self, one_hot):

test/algorithms/classifiers/test_pegasos_qsvc.py

@@ -227,9 +227,9 @@ def test_save_load(self):
 
         # save/load, change the quantum instance and check if predicted values are the same
         file_name = os.path.join(tempfile.gettempdir(), "pegasos.model")
-        regressor.save(file_name)
+        regressor.to_dill(file_name)
         try:
-            regressor_load = PegasosQSVC.load(file_name)
+            regressor_load = PegasosQSVC.from_dill(file_name)
             loaded_model_predicts = regressor_load.predict(test_features)
 
             np.testing.assert_array_almost_equal(original_predicts, loaded_model_predicts)
@@ -241,7 +241,7 @@ class FakeModel(SerializableModelMixin):
                 pass
 
             with self.assertRaises(TypeError):
-                FakeModel.load(file_name)
+                FakeModel.from_dill(file_name)
 
         finally:
             os.remove(file_name)

test/algorithms/classifiers/test_qsvc.py

@@ -107,9 +107,9 @@ def test_save_load(self):
 
         # save/load, change the quantum instance and check if predicted values are the same
         file_name = os.path.join(tempfile.gettempdir(), "qsvc.model")
-        classifier.save(file_name)
+        classifier.to_dill(file_name)
         try:
-            classifier_load = QSVC.load(file_name)
+            classifier_load = QSVC.from_dill(file_name)
             loaded_model_predicts = classifier_load.predict(test_features)
 
             np.testing.assert_array_almost_equal(original_predicts, loaded_model_predicts)
@@ -121,7 +121,7 @@ class FakeModel(SerializableModelMixin):
                 pass
 
             with self.assertRaises(TypeError):
-                FakeModel.load(file_name)
+                FakeModel.from_dill(file_name)
 
         finally:
             os.remove(file_name)

test/algorithms/regressors/test_fidelity_quantum_kernel_qsvr.py

@@ -136,9 +136,9 @@ def test_save_load(self):
 
         with tempfile.TemporaryDirectory() as dir_name:
             file_name = os.path.join(dir_name, "qsvr.model")
-            regressor.save(file_name)
+            regressor.to_dill(file_name)
 
-            regressor_load = QSVR.load(file_name)
+            regressor_load = QSVR.from_dill(file_name)
             loaded_model_predicts = regressor_load.predict(test_features)
 
             np.testing.assert_array_almost_equal(original_predicts, loaded_model_predicts)
@@ -149,7 +149,7 @@ class FakeModel(SerializableModelMixin):
                 pass
 
             with self.assertRaises(TypeError):
-                FakeModel.load(file_name)
+                FakeModel.from_dill(file_name)
 
 
 if __name__ == "__main__":

test/algorithms/regressors/test_neural_network_regressor.py

@@ -189,9 +189,9 @@ def test_save_load(self):
         # save/load, change the quantum instance and check if predicted values are the same
         with tempfile.TemporaryDirectory() as dir_name:
             file_name = os.path.join(dir_name, "regressor.model")
-            regressor.save(file_name)
+            regressor.to_dill(file_name)
 
-            regressor_load = NeuralNetworkRegressor.load(file_name)
+            regressor_load = NeuralNetworkRegressor.from_dill(file_name)
             loaded_model_predicts = regressor_load.predict(test_features)
 
             np.testing.assert_array_almost_equal(original_predicts, loaded_model_predicts)
@@ -203,7 +203,7 @@ class FakeModel(SerializableModelMixin):
                 pass
 
             with self.assertRaises(TypeError):
-                FakeModel.load(file_name)
+                FakeModel.from_dill(file_name)
 
     def test_untrained(self):
         """Test untrained regressor."""

test/algorithms/regressors/test_qsvr.py

@@ -131,9 +131,9 @@ def test_save_load(self):
 
         with tempfile.TemporaryDirectory() as dir_name:
             file_name = os.path.join(dir_name, "qsvr.model")
-            regressor.save(file_name)
+            regressor.to_dill(file_name)
 
-            regressor_load = QSVR.load(file_name)
+            regressor_load = QSVR.from_dill(file_name)
             loaded_model_predicts = regressor_load.predict(test_features)
 
             np.testing.assert_array_almost_equal(original_predicts, loaded_model_predicts)
@@ -144,7 +144,7 @@ class FakeModel(SerializableModelMixin):
                 pass
 
             with self.assertRaises(TypeError):
-                FakeModel.load(file_name)
+                FakeModel.from_dill(file_name)
 
 
 if __name__ == "__main__":