Remove deprecated qubit auto-alignment (#1024)

qiskit_machine_learning/algorithms/classifiers/vqc.py

@@ -49,7 +49,6 @@ class VQC(NeuralNetworkClassifier):
     # pylint: disable=too-many-positional-arguments
     def __init__(
         self,
-        num_qubits: int | None = None,
         feature_map: QuantumCircuit | None = None,
         ansatz: QuantumCircuit | None = None,
         loss: str | Loss = "cross_entropy",
@@ -65,11 +64,6 @@ def __init__(
     ) -> None:
         """
         Args:
-            num_qubits: The number of qubits for the underlying QNN.
-                If ``None`` is given, the number of qubits is derived from the
-                feature map or ansatz. If neither of those is given, raises an exception.
-                The number of qubits in the feature map and ansatz are adjusted to this
-                number if required and possible (such adjustment is deprecated).
             feature_map: The (parametrized) circuit to be used as a feature map for the underlying
                 QNN. If ``None`` is given, the :meth:`~qiskit.circuit.library.zz_feature_map`
                 is used if the number of qubits is larger than 1. For a single qubit
@@ -102,14 +96,11 @@ def __init__(
             output_shape: The output shape for the underlying neural network, generally equals to
                 number of classes. Defaults to binary classification, 2.
         Raises:
-            QiskitMachineLearningError: Needs at least one out of ``num_qubits``, ``feature_map`` or
-                ``ansatz`` to be given. Or the number of qubits in the feature map and/or ansatz
-                can't be adjusted to ``num_qubits``.
+            QiskitMachineLearningError: At least one of ``feature_map`` or ``ansatz`` must be
+                given, or the number of qubits in the feature map and ansatz must match.
         """
 
-        num_qubits, feature_map, ansatz = derive_num_qubits_feature_map_ansatz(
-            num_qubits, feature_map, ansatz
-        )
+        num_qubits, feature_map, ansatz = derive_num_qubits_feature_map_ansatz(feature_map, ansatz)
 
         if output_shape is None:
             self.output_shape = 2

qiskit_machine_learning/algorithms/regressors/vqr.py

@@ -35,7 +35,6 @@ class VQR(NeuralNetworkRegressor):
     # pylint: disable=too-many-positional-arguments
     def __init__(
         self,
-        num_qubits: int | None = None,
         feature_map: QuantumCircuit | None = None,
         ansatz: QuantumCircuit | None = None,
         observable: BaseOperator | None = None,
@@ -50,11 +49,6 @@ def __init__(
     ) -> None:
         r"""
         Args:
-            num_qubits: The number of qubits for the underlying QNN.
-                If ``None`` then the number of qubits is derived from the
-                feature map or ansatz, but if neither of these are given an error is raised.
-                The number of qubits in the feature map and ansatz are adjusted to this
-                number if required and possible (such adjustment is deprecated).
             feature_map: The (parametrized) circuit to be used as a feature map for the underlying
                 QNN. If ``None`` the :meth:`~qiskit.circuit.library.zz_feature_map`
                 is used if the number of qubits is larger than 1. For a single qubit regression
@@ -82,9 +76,8 @@ def __init__(
             pass_manager: The pass manager to transpile the circuits, if necessary.
                 Defaults to ``None``, as some primitives do not need transpiled circuits.
         Raises:
-            QiskitMachineLearningError: Needs at least one out of ``num_qubits``, ``feature_map`` or
-                ``ansatz`` to be given. Or the number of qubits in the feature map and/or ansatz
-                can't be adjusted to ``num_qubits``.
+            QiskitMachineLearningError: At least one of ``feature_map`` or ``ansatz`` must be
+                given, or the number of qubits in the feature map and ansatz must match.
             ValueError: if the type of the observable is not compatible with ``estimator``.
         """
         if observable is not None and not isinstance(observable, BaseOperator):
@@ -95,9 +88,7 @@ def __init__(
 
         self._estimator = estimator
 
-        num_qubits, feature_map, ansatz = derive_num_qubits_feature_map_ansatz(
-            num_qubits, feature_map, ansatz
-        )
+        num_qubits, feature_map, ansatz = derive_num_qubits_feature_map_ansatz(feature_map, ansatz)
 
         # construct circuit
         self._feature_map = feature_map

qiskit_machine_learning/circuit/library/qnn_circuit.py

@@ -21,7 +21,6 @@
 
 
 def qnn_circuit(
-    num_qubits: int | None = None,
     feature_map: QuantumCircuit | None = None,
     ansatz: QuantumCircuit | None = None,
 ):
@@ -31,12 +30,11 @@ def qnn_circuit(
     use for inputs and weights as needed for a neural network, such as
     :class:`~qiskit-machine-learning.neural_networks.SamplerQNN`.
 
-    If only the number of qubits is provided the :meth:`~qiskit.circuit.library.real_amplitudes`
-    ansatz and the :meth:`~qiskit.circuit.library.zz_feature_map` feature map are used. If the
-    number of qubits is 1 the :meth:`~qiskit.circuit.library.z_feature_map` is used. If only a
-    feature map is provided, the :meth:`~qiskit.circuit.library.real_amplitudes` ansatz with the
-    corresponding number of qubits is used. If only an ansatz is provided the
+    If only a feature map is provided, the :meth:`~qiskit.circuit.library.real_amplitudes`
+    ansatz with the corresponding number of qubits is used. If only an ansatz is provided the
     :meth:`~qiskit.circuit.library.zz_feature_map` with the corresponding number of qubits is used.
+    For a single qubit, :meth:`~qiskit.circuit.library.z_feature_map` is used as the default feature
+    map.
 
     At least one parameter has to be provided. If a feature map and an ansatz is provided, the
     number of qubits must be the same.
@@ -45,8 +43,9 @@ def qnn_circuit(
 
     .. code-block:: python
 
+        from qiskit.circuit.library import zz_feature_map
         from qiskit_machine_learning.circuit.library import qnn_circuit
-        qnn_qc, fm_params, anz_params = qnn_circuit(2)
+        qnn_qc, fm_params, anz_params = qnn_circuit(feature_map=zz_feature_map(2))
         qnn_qc.draw(fold=60)
         #      ┌───┐┌─────────────┐     »
         # q_0: ┤ H ├┤ P(2.0*x[0]) ├──■──»
@@ -81,42 +80,24 @@ def qnn_circuit(
         #                ParameterVectorElement(θ[4]), ParameterVectorElement(θ[5]),
         #                ParameterVectorElement(θ[6]), ParameterVectorElement(θ[7])])
 
-    Although all arguments to qnn_circuit default to None at least one must be provided,
-    to determine the number of qubits from.
-
-    If more than one is passed:
-
-    1) If num_qubits is provided the feature map and/or ansatz circuits supplied must be the
-    same number of qubits.
-
-    2) If num_qubits is not provided the feature_map and ansatz must be set to the same number
-    of qubits.
-
     Args:
-        num_qubits:  Number of qubits, a positive integer. Optional if feature_map or ansatz is
-                     provided, otherwise required. If not provided num_qubits defaults from the
-                     sizes of feature_map and/or ansatz.
-        feature_map: A feature map. Optional if num_qubits or ansatz is provided, otherwise
-                     required. If not provided defaults to
-                     :meth:`~qiskit.circuit.library.zz_feature_map` or
-                     :meth:`~qiskit.circuit.library.z_feature_map` if num_qubits is determined
-                     to be 1.
-        ansatz:      An ansatz. Optional if num_qubits or feature_map is provided, otherwise
-                     required. If not provided defaults to
-                     :meth:`~qiskit.circuit.library.real_amplitudes`.
+        feature_map: A feature map. At least one of ``feature_map`` or ``ansatz`` must be
+            provided. If not provided defaults to
+            :meth:`~qiskit.circuit.library.zz_feature_map` or
+            :meth:`~qiskit.circuit.library.z_feature_map` for a single qubit.
+        ansatz: An ansatz. At least one of ``feature_map`` or ``ansatz`` must be provided.
+            If not provided defaults to :meth:`~qiskit.circuit.library.real_amplitudes`.
 
     Returns:
         The composed feature map and ansatz circuit, the feature map parameters and the
         ansatz parameters.
 
     Raises:
         QiskitMachineLearningError: If a valid number of qubits cannot be derived from the \
-        provided input arguments.
+        provided input arguments, or if ``feature_map`` and ``ansatz`` have different numbers of \
+        qubits.
     """
-    # Check if circuit is constructed with valid configuration and set properties accordingly.
-    num_qubits, feature_map, ansatz = derive_num_qubits_feature_map_ansatz(
-        num_qubits, feature_map, ansatz
-    )
+    num_qubits, feature_map, ansatz = derive_num_qubits_feature_map_ansatz(feature_map, ansatz)
     qc = QuantumCircuit(num_qubits)
     qc.compose(feature_map, inplace=True)
     qc.compose(ansatz, inplace=True)

qiskit_machine_learning/kernels/base_kernel.py

@@ -47,10 +47,8 @@ def __init__(self, *, feature_map: QuantumCircuit = None, enforce_psd: bool = Tr
         """
         Args:
             feature_map: Parameterized circuit to be used as the feature map. This is required: if
-                ``None`` is given, a :class:`~QiskitMachineLearningError` is raised. If there's
-                a mismatch in the number of qubits of the feature map and the number of features
-                in the dataset, then the kernel will try to adjust the feature map to reflect the
-                number of features.
+                ``None`` is given, a :class:`~QiskitMachineLearningError` is raised. The number of
+                features in the input data must match the number of qubits in the feature map.
             enforce_psd: Project to the closest positive semidefinite matrix if ``x = y``.
                 Default ``True``.
         """
@@ -111,16 +109,12 @@ def _validate_input(
             x_vec = np.reshape(x_vec, (-1, len(x_vec)))
 
         if x_vec.shape[1] != self._num_features:
-            # before raising an error we try to adjust the feature map
-            # to the required number of qubit.
-            try:
-                self._feature_map.num_qubits = x_vec.shape[1]
-            except AttributeError as a_e:
-                raise ValueError(
-                    f"x_vec and class feature map have incompatible dimensions.\n"
-                    f"x_vec has {x_vec.shape[1]} dimensions, "
-                    f"but feature map has {self._num_features}."
-                ) from a_e
+            raise ValueError(
+                f"x_vec and feature map have incompatible dimensions.\n"
+                f"x_vec has {x_vec.shape[1]} features, but the feature map expects "
+                f"{self._num_features} parameters. "
+                "Construct the feature map with a matching feature dimension."
+            )
 
         if y_vec is not None:
             y_vec = np.asarray(y_vec)

qiskit_machine_learning/neural_networks/estimator_qnn.py

@@ -67,7 +67,7 @@ class EstimatorQNN(NeuralNetwork):
         # Using the qnn_circuit:
         # Create a parametrized 2 qubit circuit composed of the default zz_feature_map feature map
         # and real_amplitudes ansatz.
-        qnn_qc, fm_params, anz_params = qnn_circuit(num_qubits)
+        qnn_qc, fm_params, anz_params = qnn_circuit(feature_map=zz_feature_map(num_qubits))
 
         qnn = EstimatorQNN(
             circuit=qnn_qc,

qiskit_machine_learning/neural_networks/sampler_qnn.py

@@ -102,7 +102,7 @@ def parity(x):
 
         # Example 1: Using the qnn_circuit
         # qnn_circuit automatically combines a feature map and an ansatz into a single circuit
-        qnn_qc, fm_params, anz_params = qnn_circuit(num_qubits)
+        qnn_qc, fm_params, anz_params = qnn_circuit(feature_map=zz_feature_map(num_qubits))
 
         qnn = SamplerQNN(
             circuit=qnn_qc,