Use cross-references to Terra and QML objects in docstrings

qiskit_machine_learning/algorithms/classifiers/pegasos_qsvc.py

@@ -67,8 +67,9 @@ def __init__(
     ) -> None:
         """
         Args:
-            quantum_kernel: a quantum kernel to be used for classification. Has to be ``None`` when
-                a precomputed kernel is used.
+            quantum_kernel: A subclass of :class:`~qiskit_machine_learning.kernels.BaseKernel`
+                to be used for classification.
+                Has to be ``None`` when a precomputed kernel is used.
             C: Positive regularization parameter. The strength of the regularization is inversely
                 proportional to C. Smaller ``C`` induce smaller weights which generally helps
                 preventing overfitting. However, due to the nature of this algorithm, some of the
@@ -87,9 +88,7 @@ def __init__(
             ValueError:
                 - if ``quantum_kernel`` is passed and ``precomputed`` is set to ``True``. To use
                 a precomputed kernel, ``quantum_kernel`` has to be of the ``None`` type.
-            TypeError:
-                - if ``quantum_kernel`` neither instance of
-                  :class:`~qiskit_machine_learning.kernels.BaseKernel` nor ``None``.
+                - if C is not a positive number.
         """
 
         if precomputed:

qiskit_machine_learning/algorithms/classifiers/qsvc.py

@@ -45,7 +45,9 @@ class QSVC(SVC, SerializableModelMixin):
     def __init__(self, *, quantum_kernel: Optional[BaseKernel] = None, **kwargs):
         """
         Args:
-            quantum_kernel: Quantum kernel to be used for classification.
+            quantum_kernel: A subclass of :class:`~qiskit_machine_learning.kernels.BaseKernel`
+                to be used for classification.
+                If None, default to :class:`~qiskit_machine_learning.kernels.FidelityQuantumKernel`.
             *args: Variable length argument list to pass to SVC constructor.
             **kwargs: Arbitrary keyword arguments to pass to SVC constructor.
         """

qiskit_machine_learning/algorithms/classifiers/vqc.py

@@ -65,11 +65,13 @@ def __init__(
                 The number of qubits in the feature map and ansatz are adjusted to this
                 number if required.
             feature_map: The (parametrized) circuit to be used as a feature map for the underlying
-                QNN. If ``None`` is given, the ``ZZFeatureMap`` is used if the number of qubits
-                is larger than 1. For a single qubit classification problem the ``ZFeatureMap``
+                QNN. If ``None`` is given, the :class:`~qiskit.circuit.library.ZZFeatureMap`
+                is used if the number of qubits is larger than 1. For a single qubit
+                classification problem the :class:`~qiskit.circuit.library.ZFeatureMap`
                 is used by default.
-            ansatz: The (parametrized) circuit to be used as an ansatz for the underlying
-                QNN. If ``None`` is given then the ``RealAmplitudes`` circuit is used.
+            ansatz: The (parametrized) circuit to be used as an ansatz for the underlying QNN.
+                If ``None`` is given then the :class:`~qiskit.circuit.library.RealAmplitudes`
+                circuit is used.
             loss: A target loss function to be used in training. Default value is ``cross_entropy``.
             optimizer: An instance of an optimizer or a callable to be used in training.
                 Refer to :class:`~qiskit_algorithms.optimizers.Minimizer` for more information on

qiskit_machine_learning/algorithms/regressors/qsvr.py

@@ -44,7 +44,9 @@ class QSVR(SVR, SerializableModelMixin):
     def __init__(self, *, quantum_kernel: Optional[BaseKernel] = None, **kwargs):
         """
         Args:
-            quantum_kernel: Quantum kernel to be used for regression.
+            quantum_kernel: A subclass of :class:`~qiskit_machine_learning.kernels.BaseKernel`
+                to be used for regression.
+                If None, default to :class:`~qiskit_machine_learning.kernels.FidelityQuantumKernel`.
             *args: Variable length argument list to pass to SVR constructor.
             **kwargs: Arbitrary keyword arguments to pass to SVR constructor.
         """

qiskit_machine_learning/datasets/ad_hoc.py

@@ -37,7 +37,7 @@ def ad_hoc_data(
     np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray
 ]:
     r"""Generates a toy dataset that can be fully separated with
-    ``qiskit.circuit.library.ZZ_Feature_Map`` according to the procedure
+    :class:`~qiskit.circuit.library.ZZFeatureMap` according to the procedure
     outlined in [1]. To construct the dataset, we first sample uniformly
     distributed vectors :math:`\vec{x} \in (0, 2\pi]^{n}` and apply the
     feature map

qiskit_machine_learning/kernels/fidelity_statevector_kernel.py

@@ -40,7 +40,8 @@ class FidelityStatevectorKernel(BaseKernel):
 
         K(x,y) = |\langle \phi(x) | \phi(y) \rangle|^2.
 
-    In this implementation, :math:`|\phi(y)\rangle` is given by a ``Statevector.data`` array. These
+    In this implementation, :math:`|\phi(y)\rangle` is given by a
+    :attr:`qiskit.quantum_info.Statevector.data` array. These
     arrays are stored in a statevector cache to avoid repeated evaluation of the quantum circuit.
     This cache can be cleared using :meth:`clear_cache`. By default the cache is cleared when
     :meth:`evaluate` is called, unless ``auto_clear_cache`` is ``False``.

qiskit_machine_learning/utils/adjust_num_qubits.py

@@ -30,16 +30,17 @@ def derive_num_qubits_feature_map_ansatz(
     Derives a correct number of qubits, feature map, and ansatz from the parameters.
 
     If the number of qubits is not ``None``, then the feature map and ansatz are adjusted to this
-    number of qubits if required. If such an adjustment fails, an error is raised.
-    Also, if the feature map or ansatz or both are ``None``, then ``ZZFeatureMap`` and
-    ``RealAmplitudes`` are created respectively. If there's just one qubit, ``ZFeatureMap`` is
-    created instead.
+    number of qubits if required. If such an adjustment fails, an error is raised. Also, if the
+    feature map or ansatz or both are ``None``, then :class:`~qiskit.circuit.library.ZZFeatureMap`
+    and :class:`~qiskit.circuit.library.RealAmplitudes` are created respectively. If there's just
+    one qubit, :class:`~qiskit.circuit.library.ZFeatureMap` is created instead.
 
     If the number of qubits is ``None``, then the number of qubits is derived from the feature map
     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
-    ``ZZFeatureMap`` or ``RealAmplitudes`` are created respectively.
+    :class:`~qiskit.circuit.library.ZZFeatureMap` or :class:`~qiskit.circuit.library.RealAmplitudes`
+    are created respectively.
 
     If all the parameters are none an error is raised.
 

qiskit_machine_learning/utils/loss_functions/kernel_loss_functions.py

@@ -55,7 +55,8 @@ def evaluate(
 
         Args:
             parameter_values: an array of values to assign to the user params
-            quantum_kernel: A ``QuantumKernel`` object to evaluate
+            quantum_kernel: A :class:`~qiskit_machine_learning.kernels.BaseKernel` object
+                to evaluate
             data: An ``(N, M)`` matrix containing the data
                     ``N = # samples, M = dimension of data``
             labels: A length-N array containing the truth labels