Add QuantumKernel class using terra primitives (#437)

* Add primitives branch to CI workflow

* added pocs

* init fixed

* more init

* Add qnns PoC

* lint fixed

* Migrate qnns to new branch

* some unittests

* more unittest

* adapted new design without unittests

* removed compute

* removed compute

* Change inputs, assign parameters method name

* docstrings

* working on dict version

* support dictionarys

* Update qiskit_machine_learning/primitives/kernels/base_kernel.py

Co-authored-by: dlasecki <dal@zurich.ibm.com>

* Update qiskit_machine_learning/primitives/kernels/base_kernel.py

Co-authored-by: dlasecki <dal@zurich.ibm.com>

* Small fixes

* Remove changes from trailing commit

* Update qiskit_machine_learning/utils/utils.py

Co-authored-by: dlasecki <dal@zurich.ibm.com>

* raise error if not all parameters bound

* typo

* unit tests

* training unit tests

* feature params order fixex

* lint

* Create trainable mixin

* Update qiskit_machine_learning/primitives/kernels/base_kernel.py

Co-authored-by: dlasecki <dal@zurich.ibm.com>

* Update qiskit_machine_learning/primitives/kernels/base_kernel.py

Co-authored-by: dlasecki <dal@zurich.ibm.com>

* Update qiskit_machine_learning/primitives/kernels/pseudo_kernel.py

Co-authored-by: dlasecki <dal@zurich.ibm.com>

* Fix fidelity imports (new location)

* Remove pseudo kernel

* evaluate duplicates

* merge

* black

* fix some issues

* update to the recent interfaces

* update tests

* fix fidelity parameterization

* skip some tests

* fix pylint, mypy

* fix copyright

* more tweaks

* rename methods

* refactor

* clean up

* some docstrings

* more tests

* streamline base class

* fix spell

* trainable qk tweaks

* fix trainability

* revert workflows

* revert the notebook

* renamings, refactorings, code review

* code review

* remove commented code

* bump terra requirements

* code review

* code review

* fix pylint

* enforce psd test

* fix docstring

* add reno

* fix documentation reference

* update qsvc, more tests

* update qsvr, even more tests

* fix qsvr tests

* fix lint

* Change Terra requirements to accept rc

* update pegasos

* fix test

* Update qiskit_machine_learning/algorithms/classifiers/pegasos_qsvc.py

Co-authored-by: Gian Gentinetta <31244916+gentinettagian@users.noreply.github.com>

* fix complex eigenvalues

* pass parameters as keywords

* fix trainability, code review

* mixin -> abstract class, keywords

* fix docs

* update docs

* code review

* fix tests, pylint

* update reno

* update reno

* fix reno

* Update qiskit_machine_learning/algorithms/classifiers/qsvc.py

Co-authored-by: Steve Wood <40241007+woodsp-ibm@users.noreply.github.com>

* Update qiskit_machine_learning/algorithms/regressors/qsvr.py

Co-authored-by: Steve Wood <40241007+woodsp-ibm@users.noreply.github.com>

* Update qiskit_machine_learning/kernels/quantum_kernel.py

Co-authored-by: Steve Wood <40241007+woodsp-ibm@users.noreply.github.com>

* Update releasenotes/notes/add-fidelity-quantum-kernel-d40278abb49e19b5.yaml

Co-authored-by: Steve Wood <40241007+woodsp-ibm@users.noreply.github.com>

* code review

* fix tests

* terra 0.22

* more tests

Co-authored-by: Manoel Marques <manoel.marques@ibm.com>
Co-authored-by: Gian Gentinetta <gian.gentinetta@gmx.ch>
Co-authored-by: dlasecki <dal@zurich.ibm.com>
Co-authored-by: Anton Dekusar <adekusar@ie.ibm.com>
Co-authored-by: Anton Dekusar <62334182+adekusar-drl@users.noreply.github.com>
Co-authored-by: Gian Gentinetta <31244916+gentinettagian@users.noreply.github.com>
Co-authored-by: Steve Wood <40241007+woodsp-ibm@users.noreply.github.com>

.pylintdict

@@ -6,6 +6,7 @@ ansatz
 ansatzes
 args
 asmatrix
+async
 autograd
 autosummary
 backend
@@ -62,6 +63,7 @@ et
 eval
 expressibility
 farrokh
+fidelities
 formatter
 func
 gambetta

qiskit_machine_learning/algorithms/classifiers/pegasos_qsvc.py

@@ -11,18 +11,20 @@
 # that they have been altered from the originals.
 
 """Pegasos Quantum Support Vector Classifier."""
+from __future__ import annotations
 
 import logging
 from datetime import datetime
-from typing import Optional, Dict
+from typing import Dict
 
 import numpy as np
 from qiskit.utils import algorithm_globals
 from sklearn.base import ClassifierMixin
 
 from ...algorithms.serializable_model import SerializableModelMixin
 from ...exceptions import QiskitMachineLearningError
-from ...kernels.quantum_kernel import QuantumKernel
+from ...kernels import BaseKernel, FidelityQuantumKernel
+
 
 logger = logging.getLogger(__name__)
 
@@ -38,7 +40,7 @@ class PegasosQSVC(ClassifierMixin, SerializableModelMixin):
 
     .. code-block:: python
 
-        quantum_kernel = QuantumKernel()
+        quantum_kernel = FidelityQuantumKernel()
 
         pegasos_qsvc = PegasosQSVC(quantum_kernel=quantum_kernel)
         pegasos_qsvc.fit(sample_train, label_train)
@@ -56,15 +58,15 @@ class PegasosQSVC(ClassifierMixin, SerializableModelMixin):
     # pylint: disable=invalid-name
     def __init__(
         self,
-        quantum_kernel: Optional[QuantumKernel] = None,
+        quantum_kernel: BaseKernel | None = None,
         C: float = 1.0,
         num_steps: int = 1000,
         precomputed: bool = False,
-        seed: Optional[int] = None,
+        seed: int | None = None,
     ) -> None:
         """
         Args:
-            quantum_kernel: QuantumKernel to be used for classification. Has to be ``None`` when
+            quantum_kernel: a quantum kernel 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
@@ -85,17 +87,16 @@ def __init__(
                 - 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_instance`` neither instance of ``QuantumKernel`` nor ``None``.
+                - if ``quantum_kernel`` neither instance of
+                  :class:`~qiskit_machine_learning.kernels.BaseKernel` nor ``None``.
         """
 
         if precomputed:
             if quantum_kernel is not None:
                 raise ValueError("'quantum_kernel' has to be None to use a precomputed kernel")
         else:
             if quantum_kernel is None:
-                quantum_kernel = QuantumKernel()
-            elif not isinstance(quantum_kernel, QuantumKernel):
-                raise TypeError("'quantum_kernel' has to be of type None or QuantumKernel")
+                quantum_kernel = FidelityQuantumKernel()
 
         self._quantum_kernel = quantum_kernel
         self._precomputed = precomputed
@@ -109,13 +110,13 @@ def __init__(
             raise ValueError(f"C has to be a positive number, found {C}.")
 
         # these are the parameters being fit and are needed for prediction
-        self._alphas: Optional[Dict[int, int]] = None
-        self._x_train: Optional[np.ndarray] = None
-        self._n_samples: Optional[int] = None
-        self._y_train: Optional[np.ndarray] = None
-        self._label_map: Optional[Dict[int, int]] = None
-        self._label_pos: Optional[int] = None
-        self._label_neg: Optional[int] = None
+        self._alphas: Dict[int, int] | None = None
+        self._x_train: np.ndarray | None = None
+        self._n_samples: int | None = None
+        self._y_train: np.ndarray | None = None
+        self._label_map: Dict[int, int] | None = None
+        self._label_pos: int | None = None
+        self._label_neg: int | None = None
 
         # added to all kernel values to include an implicit bias to the hyperplane
         self._kernel_offset = 1
@@ -125,12 +126,13 @@ def __init__(
 
     # pylint: disable=invalid-name
     def fit(
-        self, X: np.ndarray, y: np.ndarray, sample_weight: Optional[np.ndarray] = None
+        self, X: np.ndarray, y: np.ndarray, sample_weight: np.ndarray | None = None
     ) -> "PegasosQSVC":
         """Fit the model according to the given training data.
 
         Args:
-            X: Train features. For a callable kernel (an instance of ``QuantumKernel``) the shape
+            X: Train features. For a callable kernel (an instance of
+               :class:`~qiskit_machine_learning.kernels.BaseKernel`) the shape
                should be ``(n_samples, n_features)``, for a precomputed kernel the shape should be
                ``(n_samples, n_samples)``.
             y: shape (n_samples), train labels . Must not contain more than two unique labels.
@@ -206,7 +208,8 @@ def predict(self, X: np.ndarray) -> np.ndarray:
         Perform classification on samples in X.
 
         Args:
-            X: Features. For a callable kernel (an instance of ``QuantumKernel``) the shape
+            X: Features. For a callable kernel (an instance of
+               :class:`~qiskit_machine_learning.kernels.BaseKernel`) the shape
                should be ``(m_samples, n_features)``, for a precomputed kernel the shape should be
                ``(m_samples, n_samples)``. Where ``m`` denotes the set to be predicted and ``n`` the
                size of the training set. In that case, the kernel values in X have to be calculated
@@ -234,7 +237,8 @@ def decision_function(self, X: np.ndarray) -> np.ndarray:
         Evaluate the decision function for the samples in X.
 
         Args:
-            X: Features. For a callable kernel (an instance of ``QuantumKernel``) the shape
+            X: Features. For a callable kernel (an instance of
+               :class:`~qiskit_machine_learning.kernels.BaseKernel`) the shape
                should be ``(m_samples, n_features)``, for a precomputed kernel the shape should be
                ``(m_samples, n_samples)``. Where ``m`` denotes the set to be predicted and ``n`` the
                size of the training set. In that case, the kernel values in X have to be calculated
@@ -302,12 +306,12 @@ def _compute_weighted_kernel_sum(self, index: int, X: np.ndarray, training: bool
         return value
 
     @property
-    def quantum_kernel(self) -> QuantumKernel:
+    def quantum_kernel(self) -> BaseKernel:
         """Returns quantum kernel"""
         return self._quantum_kernel
 
     @quantum_kernel.setter
-    def quantum_kernel(self, quantum_kernel: QuantumKernel):
+    def quantum_kernel(self, quantum_kernel: BaseKernel):
         """
         Sets quantum kernel. If previously a precomputed kernel was set, it is reset to ``False``.
         """
@@ -340,14 +344,15 @@ def precomputed(self) -> bool:
     @precomputed.setter
     def precomputed(self, precomputed: bool):
         """Sets the pre-computed kernel flag. If ``True`` is passed then the previous kernel is
-        cleared. If ``False`` is passed then a new instance of ``QuantumKernel`` is created."""
+        cleared. If ``False`` is passed then a new instance of
+        :class:`~qiskit_machine_learning.kernels.FidelityQuantumKernel` is created."""
         self._precomputed = precomputed
         if precomputed:
             # remove the kernel, a precomputed will
             self._quantum_kernel = None
         else:
             # re-create a new default quantum kernel
-            self._quantum_kernel = QuantumKernel()
+            self._quantum_kernel = FidelityQuantumKernel()
 
         # reset training status
         self._reset_state()

qiskit_machine_learning/algorithms/classifiers/qsvc.py

@@ -20,7 +20,7 @@
 
 from qiskit_machine_learning.algorithms.serializable_model import SerializableModelMixin
 from qiskit_machine_learning.exceptions import QiskitMachineLearningWarning
-from qiskit_machine_learning.kernels.quantum_kernel import QuantumKernel
+from qiskit_machine_learning.kernels import BaseKernel, FidelityQuantumKernel
 
 
 class QSVC(SVC, SerializableModelMixin):
@@ -41,10 +41,10 @@ class QSVC(SVC, SerializableModelMixin):
         qsvc.predict(sample_test)
     """
 
-    def __init__(self, *args, quantum_kernel: Optional[QuantumKernel] = None, **kwargs):
+    def __init__(self, *args, quantum_kernel: Optional[BaseKernel] = None, **kwargs):
         """
         Args:
-            quantum_kernel: QuantumKernel to be used for classification.
+            quantum_kernel: Quantum kernel to be used for classification.
             *args: Variable length argument list to pass to SVC constructor.
             **kwargs: Arbitrary keyword arguments to pass to SVC constructor.
         """
@@ -65,20 +65,20 @@ def __init__(self, *args, quantum_kernel: Optional[QuantumKernel] = None, **kwar
             # if we don't delete, then this value clashes with our quantum kernel
             del kwargs["kernel"]
 
-        self._quantum_kernel = quantum_kernel if quantum_kernel else QuantumKernel()
+        self._quantum_kernel = quantum_kernel if quantum_kernel else FidelityQuantumKernel()
 
         if "random_state" not in kwargs:
             kwargs["random_state"] = algorithm_globals.random_seed
 
         super().__init__(kernel=self._quantum_kernel.evaluate, *args, **kwargs)
 
     @property
-    def quantum_kernel(self) -> QuantumKernel:
+    def quantum_kernel(self) -> BaseKernel:
         """Returns quantum kernel"""
         return self._quantum_kernel
 
     @quantum_kernel.setter
-    def quantum_kernel(self, quantum_kernel: QuantumKernel):
+    def quantum_kernel(self, quantum_kernel: BaseKernel):
         """Sets quantum kernel"""
         self._quantum_kernel = quantum_kernel
         self.kernel = self._quantum_kernel.evaluate

qiskit_machine_learning/algorithms/regressors/qsvr.py

@@ -17,9 +17,9 @@
 
 from sklearn.svm import SVR
 
-from ..serializable_model import SerializableModelMixin
-from ...exceptions import QiskitMachineLearningWarning
-from ...kernels.quantum_kernel import QuantumKernel
+from qiskit_machine_learning.algorithms.serializable_model import SerializableModelMixin
+from qiskit_machine_learning.exceptions import QiskitMachineLearningWarning
+from qiskit_machine_learning.kernels import BaseKernel, FidelityQuantumKernel
 
 
 class QSVR(SVR, SerializableModelMixin):
@@ -40,10 +40,10 @@ class QSVR(SVR, SerializableModelMixin):
         qsvr.predict(sample_test)
     """
 
-    def __init__(self, *args, quantum_kernel: Optional[QuantumKernel] = None, **kwargs):
+    def __init__(self, *args, quantum_kernel: Optional[BaseKernel] = None, **kwargs):
         """
         Args:
-            quantum_kernel: QuantumKernel to be used for regression.
+            quantum_kernel: Quantum kernel to be used for regression.
             *args: Variable length argument list to pass to SVR constructor.
             **kwargs: Arbitrary keyword arguments to pass to SVR constructor.
         """
@@ -64,17 +64,17 @@ def __init__(self, *args, quantum_kernel: Optional[QuantumKernel] = None, **kwar
             # if we don't delete, then this value clashes with our quantum kernel
             del kwargs["kernel"]
 
-        self._quantum_kernel = quantum_kernel if quantum_kernel else QuantumKernel()
+        self._quantum_kernel = quantum_kernel if quantum_kernel else FidelityQuantumKernel()
 
         super().__init__(kernel=self._quantum_kernel.evaluate, *args, **kwargs)
 
     @property
-    def quantum_kernel(self) -> QuantumKernel:
+    def quantum_kernel(self) -> BaseKernel:
         """Returns quantum kernel"""
         return self._quantum_kernel
 
     @quantum_kernel.setter
-    def quantum_kernel(self, quantum_kernel: QuantumKernel):
+    def quantum_kernel(self, quantum_kernel: BaseKernel):
         """Sets quantum kernel"""
         self._quantum_kernel = quantum_kernel
         self.kernel = self._quantum_kernel.evaluate

qiskit_machine_learning/kernels/__init__.py

@@ -23,6 +23,10 @@
    :nosignatures:
 
    QuantumKernel
+   BaseKernel
+   FidelityQuantumKernel
+   TrainableKernel
+   TrainableFidelityQuantumKernel
 
 Submodules
 ==========
@@ -34,5 +38,15 @@
 """
 
 from .quantum_kernel import QuantumKernel
-
-__all__ = ["QuantumKernel"]
+from .base_kernel import BaseKernel
+from .fidelity_quantum_kernel import FidelityQuantumKernel
+from .trainable_kernel import TrainableKernel
+from .trainable_fidelity_quantum_kernel import TrainableFidelityQuantumKernel
+
+__all__ = [
+    "QuantumKernel",
+    "BaseKernel",
+    "FidelityQuantumKernel",
+    "TrainableKernel",
+    "TrainableFidelityQuantumKernel",
+]

qiskit_machine_learning/kernels/algorithms/quantum_kernel_trainer.py

@@ -11,6 +11,7 @@
 # that they have been altered from the originals.
 
 """Quantum Kernel Trainer"""
+
 import copy
 from functools import partial
 from typing import Union, Optional, Sequence
@@ -22,23 +23,23 @@
 from qiskit.algorithms.variational_algorithm import VariationalResult
 from qiskit_machine_learning.utils.loss_functions import KernelLoss, SVCLoss
 
-from qiskit_machine_learning.kernels import QuantumKernel
+from qiskit_machine_learning.kernels import TrainableKernel
 
 
 class QuantumKernelTrainerResult(VariationalResult):
     """Quantum Kernel Trainer Result."""
 
     def __init__(self) -> None:
         super().__init__()
-        self._quantum_kernel: QuantumKernel = None
+        self._quantum_kernel: TrainableKernel = None
 
     @property
-    def quantum_kernel(self) -> Optional[QuantumKernel]:
+    def quantum_kernel(self) -> Optional[TrainableKernel]:
         """Return the optimized quantum kernel object."""
         return self._quantum_kernel
 
     @quantum_kernel.setter
-    def quantum_kernel(self, quantum_kernel: QuantumKernel) -> None:
+    def quantum_kernel(self, quantum_kernel: TrainableKernel) -> None:
         self._quantum_kernel = quantum_kernel
 
 
@@ -66,10 +67,9 @@ class QuantumKernelTrainer:
         for i, param in enumerate(input_params):
             qc.rz(param, qc.qubits[i])
 
-        quant_kernel = QuantumKernel(
+        quant_kernel = TrainableFidelityQuantumKernel(
             feature_map=qc,
             training_parameters=training_params,
-            quantum_instance=...
         )
 
         loss_func = ...
@@ -88,7 +88,7 @@ class QuantumKernelTrainer:
 
     def __init__(
         self,
-        quantum_kernel: QuantumKernel,
+        quantum_kernel: TrainableKernel,
         loss: Optional[Union[str, KernelLoss]] = None,
         optimizer: Optional[Optimizer] = None,
         initial_point: Optional[Sequence[float]] = None,
@@ -118,12 +118,12 @@ def __init__(
         self._set_loss(loss)
 
     @property
-    def quantum_kernel(self) -> QuantumKernel:
+    def quantum_kernel(self) -> TrainableKernel:
         """Return the quantum kernel object."""
         return self._quantum_kernel
 
     @quantum_kernel.setter
-    def quantum_kernel(self, quantum_kernel: QuantumKernel) -> None:
+    def quantum_kernel(self, quantum_kernel: TrainableKernel) -> None:
         """Set the quantum kernel."""
         self._quantum_kernel = quantum_kernel