Fix minor typos

docs/tutorials/11_quantum_convolutional_neural_networks.ipynb

@@ -808,7 +808,7 @@
    "source": [
     "In this example, we will use the COBYLA optimizer to train our classifier, which is a numerical optimization method commonly used for classification machine learning algorithms.\n",
     "\n",
-    "We then place the the callback function, optimizer and operator of our QCNN created above into Qiskit Machine Learning's built in Neural Network Classifier, which we can then use to train our model. \n",
+    "We then place the callback function, optimizer and operator of our QCNN created above into Qiskit Machine Learning's built in Neural Network Classifier, which we can then use to train our model. \n",
     "\n",
     "Since model training may take a long time we have already pre-trained the model for some iterations and saved the pre-trained weights. We'll continue training from that point by setting `initial_point` to a vector of pre-trained weights."
    ]

docs/tutorials/12_quantum_autoencoder.ipynb

@@ -13,7 +13,7 @@
    "id": "d1764d89",
    "metadata": {},
    "source": [
-    "The goal of this tutorial is to build an Quantum Autoencoder, a circuit which can compress a quantum state onto a smaller amount of qubits, while retaining the information from the initial state.\n",
+    "The goal of this tutorial is to build a Quantum Autoencoder, a circuit which can compress a quantum state onto a smaller amount of qubits, while retaining the information from the initial state.\n",
     "\n",
     "Throughout this tutorial, we explain the architecture of a Quantum Autoencoder and how one can design and train such a system to compress and encode information. Following this discussion, we give two examples to demonstrate the capabilities of such a system to compress different quantum states, as well as the ability to compress images of zeros and ones. "
    ]
@@ -756,7 +756,7 @@
    "id": "4f6f37a6",
    "metadata": {},
    "source": [
-    "One can also apply a Quantum Autoencoder to more complicated examples, such as a set of handwritten digits in order to compress the dataset. Below, we will show that we can indeed train an Quantum Autoencoder to compress such an example, giving us the ability to store data more efficiently on a Quantum Computer. \n",
+    "One can also apply a Quantum Autoencoder to more complicated examples, such as a set of handwritten digits in order to compress the dataset. Below, we will show that we can indeed train a Quantum Autoencoder to compress such an example, giving us the ability to store data more efficiently on a Quantum Computer. \n",
     "\n",
     "For this tutorial, we will build a Quantum Autoencoder for a noisy dataset containing zeros and ones, which can be seen below. \n",
     "\n",

qiskit_machine_learning/algorithms/classifiers/neural_network_classifier.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
@@ -55,7 +55,7 @@ def __init__(
     ):
         """
         Args:
-            neural_network: An instance of an quantum neural network. If the neural network has a
+            neural_network: An instance of a quantum neural network. If the neural network has a
                 one-dimensional output, i.e., `neural_network.output_shape=(1,)`, then it is
                 expected to return values in [-1, +1] and it can only be used for binary
                 classification. If the output is multi-dimensional, it is assumed that the result

qiskit_machine_learning/algorithms/objective_functions.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
@@ -46,7 +46,7 @@ def __init__(
         Args:
             X: The input data.
             y: The target values.
-            neural_network: An instance of an quantum neural network to be used by this
+            neural_network: An instance of a quantum neural network to be used by this
                 objective function.
             loss: A target loss function to be used in training.
         """

qiskit_machine_learning/kernels/trainable_kernel.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2023.
+# (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
@@ -71,7 +71,7 @@ def assign_training_parameters(
                 if key not in self._training_parameters:
                     raise ValueError(
                         f"Parameter {key} is not a trainable parameter of the feature map and "
-                        f"thus cannot be bound. Make sure {key} is provided in the the trainable "
+                        f"thus cannot be bound. Make sure {key} is provided in the trainable "
                         "parameters when initializing the kernel."
                     )
                 self._parameter_dict[key] = parameter_values[key]

qiskit_machine_learning/neural_networks/__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
@@ -14,7 +14,7 @@
 Quantum neural networks (:mod:`qiskit_machine_learning.neural_networks`)
 ========================================================================
 
-A neural network is a parametrized network which may be defined as a artificial
+A neural network is a parametrized network which may be defined as an artificial
 neural network - classical neural network - or as parametrized quantum circuits
 - quantum neural network. Furthermore, neural networks can be defined with respect
 to a discriminative or generative task.

qiskit_machine_learning/utils/adjust_num_qubits.py

@@ -59,7 +59,7 @@ def derive_num_qubits_feature_map_ansatz(
     or ansatz. Both the feature map and ansatz in this case must have the same number of qubits.
     If the number of qubits of the feature map is not the same as the number of qubits of
     the ansatz, an error is raised. If only one of the feature map and ansatz are ``None``, then
-    :meth:`~qiskit.circuit.library.zz_feature_map` or :class:`~qiskit.circuit.library.rea_amplitudes`
+    :meth:`~qiskit.circuit.library.zz_feature_map` or :class:`~qiskit.circuit.library.real_amplitudes`
     are created respectively.
 
     If all the parameters are none an error is raised.
@@ -83,7 +83,7 @@ def derive_num_qubits_feature_map_ansatz(
         issue_deprecation_msg(
             msg="Using BlueprintCircuit based classes is deprecated",
             version="0.9.0",
-            remedy="Use QnnCircuit (instead) of QNNCircuit or is you "
+            remedy="Use QnnCircuit (instead) of QNNCircuit or if you "
             "are using this method directly set use_methods to True. "
             "When using methods later adjustment of the number of qubits is not "
             "possible and if not as circuits based on BlueprintCircuit, "