Deprecate QNNs (#506)

* some deprecations

* suppress warnings, more tests

* suppress warnings

* suppress warnings

* more tests

* update doc, black

* proper deprecations, suppress warnings, more tests

* fix mypy, add reno

* update reno

Author: adekusar-drl

qiskit_machine_learning/algorithms/regressors/vqr.py

@@ -99,7 +99,6 @@ def __init__(
                 ``estimator``.
         """
         # needed for mypy
-        neural_network: EstimatorQNN | TwoLayerQNN = None
         if quantum_instance is not None and estimator is None:
             warn_deprecated(
                 "0.5.0", DeprecatedType.ARGUMENT, old_name="quantum_instance", new_name="estimator"

qiskit_machine_learning/neural_networks/circuit_qnn.py

@@ -23,7 +23,7 @@
 from qiskit.circuit import Parameter
 from qiskit.opflow import Gradient, CircuitSampler, StateFn, OpflowError, OperatorBase
 from qiskit.providers import Backend
-from qiskit.utils import QuantumInstance
+from qiskit.utils import QuantumInstance, deprecate_function
 
 import qiskit_machine_learning.optionals as _optionals
 from .sampling_neural_network import SamplingNeuralNetwork
@@ -46,8 +46,16 @@ class SparseArray:  # type: ignore
 
 
 class CircuitQNN(SamplingNeuralNetwork):
-    """A sampling neural network based on a given quantum circuit."""
-
+    """Pending deprecation: A sampling neural network based on a given quantum circuit."""
+
+    @deprecate_function(
+        "The CircuitQNN class has been superseded by the "
+        "qiskit_machine_learning.neural_networks.SamplerQNN "
+        "This class will be deprecated in a future release and subsequently "
+        "removed after that.",
+        stacklevel=3,
+        category=PendingDeprecationWarning,
+    )
     def __init__(
         self,
         circuit: QuantumCircuit,

qiskit_machine_learning/neural_networks/opflow_qnn.py

@@ -27,7 +27,7 @@
     ComposedOp,
 )
 from qiskit.providers import Backend
-from qiskit.utils import QuantumInstance
+from qiskit.utils import QuantumInstance, deprecate_function
 from qiskit.utils.backend_utils import is_aer_provider
 import qiskit_machine_learning.optionals as _optionals
 from .neural_network import NeuralNetwork
@@ -50,8 +50,16 @@ class SparseArray:  # type: ignore
 
 
 class OpflowQNN(NeuralNetwork):
-    """Opflow Quantum Neural Network."""
-
+    """Pending deprecation: Opflow Quantum Neural Network."""
+
+    @deprecate_function(
+        "The OpflowQNN class has been superseded by the "
+        "qiskit_machine_learning.neural_networks.EstimatorQNN "
+        "This class will be deprecated in a future release and subsequently "
+        "removed after that.",
+        stacklevel=3,
+        category=PendingDeprecationWarning,
+    )
     def __init__(
         self,
         operator: OperatorBase,

qiskit_machine_learning/neural_networks/two_layer_qnn.py

@@ -18,17 +18,25 @@
 from qiskit import QuantumCircuit
 from qiskit.opflow import PauliSumOp, StateFn, OperatorBase, ExpectationBase
 from qiskit.providers import Backend
-from qiskit.utils import QuantumInstance
+from qiskit.utils import QuantumInstance, deprecate_function
 
 from .opflow_qnn import OpflowQNN
 from ..utils import derive_num_qubits_feature_map_ansatz
 
 
 class TwoLayerQNN(OpflowQNN):
-    """Two Layer Quantum Neural Network consisting of a feature map, a ansatz,
+    """Pending deprecation: Two Layer Quantum Neural Network consisting of a feature map, a ansatz,
     and an observable.
     """
 
+    @deprecate_function(
+        "The TwoLayerQNN class is pending deprecation and has no direct replacement. Make use of "
+        "qiskit_machine_learning.neural_networks.EstimatorQNN instead."
+        "This class will be deprecated in a future release and subsequently "
+        "removed after that.",
+        stacklevel=3,
+        category=PendingDeprecationWarning,
+    )
     def __init__(
         self,
         num_qubits: int | None = None,

releasenotes/notes/pending-deprecate-qnns-ef003277a841096d.yaml

@@ -0,0 +1,14 @@
+---
+deprecations:
+  - |
+    Class :class:`qiskit_machine_learning.neural_networks.CircuitQNN` is pending deprecation and
+    is superseded by :class:`qiskit_machine_learning.neural_networks.SamplerQNN`.
+
+    Class :class:`qiskit_machine_learning.neural_networks.OpflowQNN` is pending deprecation and
+    is superseded by :class:`qiskit_machine_learning.neural_networks.EstimatorQNN`.
+
+    Class :class:`qiskit_machine_learning.neural_networks.TwoLayerQNN` is pending deprecation and
+    has no direct replacement. Please make use of
+    :class:`qiskit_machine_learning.neural_networks.EstimatorQNN` instead.
+
+    These classes will be deprecated in a future release and subsequently removed after that.

releasenotes/notes/pending-deprecate-quantumkernel-cc56523e5e5581bf.yaml

@@ -4,3 +4,5 @@ deprecations:
     Class :class:`qiskit_machine_learning.kernels.QuantumKernel` is pending deprecation and
     is superseded by :class:`qiskit_machine_learning.kernels.FidelityQuantumKernel` and
     :class:`qiskit_machine_learning.kernels.TrainableFidelityQuantumKernel`.
+
+    This class will be deprecated in a future release and subsequently removed after that.

test/algorithms/classifiers/test_neural_network_classifier.py

@@ -14,6 +14,7 @@
 import os
 import tempfile
 import unittest
+import warnings
 from typing import Tuple, Optional, Callable
 
 from test import QiskitMachineLearningTestCase
@@ -52,6 +53,7 @@ class TestNeuralNetworkClassifier(QiskitMachineLearningTestCase):
     @unittest.skipUnless(optionals.HAS_AER, "qiskit-aer is required to run this test")
     def setUp(self):
         super().setUp()
+        warnings.filterwarnings("ignore", category=PendingDeprecationWarning)
 
         # specify quantum instances
         algorithm_globals.random_seed = 12345
@@ -69,6 +71,10 @@ def setUp(self):
             seed_transpiler=algorithm_globals.random_seed,
         )
 
+    def tearDown(self) -> None:
+        super().tearDown()
+        warnings.filterwarnings("always", category=PendingDeprecationWarning)
+
     def _create_optimizer(self, opt: str) -> Optional[Optimizer]:
         if opt == "bfgs":
             optimizer = L_BFGS_B(maxiter=5)