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fix cross reference of classes from terra #499

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fix cross reference of classes from terra #499

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4b24b02
fix cross reference of classes from terra
Ryand1234 Oct 25, 2022
6e1ff4d
fix zfeaturemap reference
Ryand1234 Oct 25, 2022
c936389
fix zfeaturemap reference
Ryand1234 Oct 25, 2022
5be3ccc
Merge branch 'update_terra_class_references' of https://github.com/Ry…
Ryand1234 Oct 25, 2022
01f6539
fix cross reference of classes from terra
Ryand1234 Oct 26, 2022
f95ca64
fix zfeaturemap reference
Ryand1234 Oct 26, 2022
42f2cb4
fix some more referencing
Ryand1234 Oct 26, 2022
9034fb9
Merge branch 'update_terra_class_references' of https://github.com/Ry…
Ryand1234 Oct 26, 2022
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10 changes: 6 additions & 4 deletions qiskit_machine_learning/algorithms/classifiers/pegasos_qsvc.py
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Expand Up @@ -84,11 +84,13 @@ def __init__(

Raises:
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.
- if :class:`~qiskit_machine_learning.kernels.FidelityQuantumKernel` is passed and
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I think this change to make things specific makes it incorrect. The quantum_kernel parameter must be None if you say you will use a precomputed kernel when that parameter is True. A quantum_kernel by type just has to be subclass of BaseKernel - so by making some specific reference it changes the meaning somewhat.

Now its true that if precomputed is False and you do not pass a quantum_kernel, i.e. you leave it at None, then it creates an instance of that specific subclass - but overall its not limited to that. Arguably this default when None and precomputed is False ought to be noted in the docstring above.

``precomputed`` is set to ``True``. To use a precomputed kernel,
:class:`~qiskit_machine_learning.kernels.FidelityQuantumKernel` has to be of
the ``None`` type.
TypeError:
- if ``quantum_kernel`` neither instance of
:class:`~qiskit_machine_learning.kernels.BaseKernel` nor ``None``.
- if :class:`~qiskit_machine_learning.kernels.FidelityQuantumKernel` neither instance
of :class:`~qiskit_machine_learning.kernels.BaseKernel` nor ``None``.
Comment on lines 91 to +93
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This I think can be completely removed - the code did raise a TypeError but that seems to have been removed in a recent change since things are now more general around a BaseKernel rather than what was there before.

On thing I do note is that is that this routine raises a ValueError if C <= 0 i.e. not a positive number, which is not included above.

"""

if precomputed:
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7 changes: 4 additions & 3 deletions qiskit_machine_learning/algorithms/classifiers/vqc.py
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Expand Up @@ -66,11 +66,12 @@ def __init__(
are adjusted to this number if required.
feature_map: The (parametrized) circuit to be used as a feature map for the underlying
:class:`~qiskit_machine_learning.neural_networks.CircuitQNN`. 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`` is used per default.
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.ZZFeatureMap` is used per default.
ansatz: The (parametrized) circuit to be used as an ansatz for the underlying
:class:`~qiskit_machine_learning.neural_networks.CircuitQNN`. If ``None`` is given
then the ``RealAmplitudes`` circuit is used.
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 to be used in training. When ``None`` defaults
to SLSQP.
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SLSQP here could be written as :class:`~qiskit.algorithms.optimizers.SLSQP` since this is the actual optimizer class it defaults to. This default is actually done in TrainableModel class where it could be altered the same way,

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7 changes: 4 additions & 3 deletions qiskit_machine_learning/algorithms/regressors/vqr.py
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Expand Up @@ -51,11 +51,12 @@ def __init__(
are adjusted to this number if required.
feature_map: The (parametrized) circuit to be used as a feature map for the underlying
:class:`~qiskit_machine_learning.neural_networks.TwoLayerQNN`. If ``None`` is given,
the ``ZZFeatureMap`` is used if the number of qubits is larger than 1. For a single
qubit regression problem the ``ZFeatureMap`` circuit is used per default.
the :class:`~qiskit.circuit.library.ZZFeatureMap` is used if the number of qubits is
larger than 1. For a single qubit regression problem the
:class:`~qiskit.circuit.library.ZZFeatureMap` circuit is used per default.
ansatz: The (parametrized) circuit to be used as an ansatz for the underlying
:class:`~qiskit_machine_learning.neural_networks.TwoLayerQNN`. If ``None`` is given
then the ``RealAmplitudes`` circuit is used.
then the :class:`~qiskit.circuit.library.RealAmplitudes` circuit is used.
observable: The observable to be measured in the underlying TwoLayerQNN. If ``None``,
use the default from the TwoLayerQNN, i.e., :math:`Z^{\otimes num\_qubits}`.
loss: A target loss function to be used in training. Default is squared error.
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3 changes: 2 additions & 1 deletion qiskit_machine_learning/neural_networks/opflow_qnn.py
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Expand Up @@ -72,7 +72,8 @@ def __init__(
quantum_instance: The quantum instance to evaluate the network.
input_gradients: Determines whether to compute gradients with respect to input data.
Note that this parameter is ``False`` by default, and must be explicitly set to
``True`` for a proper gradient computation when using ``TorchConnector``.
``True`` for a proper gradient computation when using
:class:`~qiskit_machine_learning.connectors.TorchConnector`.
"""
self._input_params = list(input_params) or []
self._weight_params = list(weight_params) or []
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3 changes: 2 additions & 1 deletion qiskit_machine_learning/neural_networks/sampling_neural_network.py
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Expand Up @@ -61,7 +61,8 @@ def __init__(
output_shape: The shape of the output.
input_gradients: Determines whether to compute gradients with respect to input data.
Note that this parameter is ``False`` by default, and must be explicitly set to
``True`` for a proper gradient computation when using ``TorchConnector``.
``True`` for a proper gradient computation when using
:class:`~qiskit_machine_learning.connectors.TorchConnector`.
Raises:
QiskitMachineLearningError: Invalid parameter values.
"""
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7 changes: 4 additions & 3 deletions qiskit_machine_learning/neural_networks/two_layer_qnn.py
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Expand Up @@ -46,10 +46,11 @@ def __init__(
is given, raises an exception. 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. If ``None`` is given,
the ``ZZFeatureMap`` is used if the number of qubits is larger than 1. For
a single qubit two-layer QNN the ``ZFeatureMap`` circuit is used per default.
the :class:`~qiskit.circuit.library.ZZFeatureMap` is used if the number of qubits is
larger than 1. For a single qubit two-layer QNN the ``ZFeatureMap`` circuit is used per
default.
ansatz: The (parametrized) circuit to be used as an ansatz. If ``None`` is given,
the ``RealAmplitudes`` circuit is used.
the :class:`~qiskit.circuit.library.RealAmplitudes` circuit is used.
observable: observable to be measured to determine the output of the network. If
``None`` is given, the :math:`Z^{\otimes num\_qubits}` observable is used.
exp_val: The Expected Value converter to be used for the operator obtained from the
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8 changes: 5 additions & 3 deletions qiskit_machine_learning/utils/adjust_num_qubits.py
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Expand Up @@ -31,15 +31,17 @@ def derive_num_qubits_feature_map_ansatz(

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
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.

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