Add QuantumKernelTrainer and KernelLoss classes

.pylintdict

@@ -104,6 +104,7 @@ ovo
 ovr
 param
 parametrized
+parametrization
 params
 pauli
 pre
@@ -182,10 +183,12 @@ utils
 variational
 vec
 vqc
+vqe
 vqr
 vx
 vy
 vz
 wrt
 zoufal
 zsh
+θ

qiskit_machine_learning/kernels/algorithms/__init__.py

@@ -0,0 +1,32 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2021.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+"""
+Quantum Kernel Algorithms (:mod:`qiskit_machine_learning.kernels.algorithms`)
+
+.. currentmodule:: qiskit_machine_learning.kernels.algorithms
+
+Quantum Kernels
+===============
+
+.. autosummary::
+   :toctree: ../stubs/
+   :nosignatures:
+
+   QuantumKernelTrainer
+   QuantumKernelTrainerResult
+
+"""
+
+from .quantum_kernel_trainer import QuantumKernelTrainer, QuantumKernelTrainerResult
+
+__all__ = ["QuantumKernelTrainer", "QuantumKernelTrainerResult"]

qiskit_machine_learning/kernels/algorithms/quantum_kernel_trainer.py

@@ -0,0 +1,231 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2021.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+"""Quantum Kernel Trainer"""
+import copy
+from functools import partial
+from typing import Union, Optional, Sequence, Callable
+
+import numpy as np
+
+from qiskit.utils.algorithm_globals import algorithm_globals
+from qiskit.algorithms.optimizers import Optimizer, SPSA
+from qiskit.algorithms.variational_algorithm import VariationalResult
+from qiskit_machine_learning.utils.loss_functions import KernelLoss, SVCLoss
+
+from qiskit_machine_learning.kernels import QuantumKernel
+
+
+class QuantumKernelTrainerResult(VariationalResult):
+    """Quantum Kernel Trainer Result."""
+
+    def __init__(self) -> None:
+        super().__init__()
+        self._quantum_kernel: QuantumKernel = None
+
+    @property
+    def quantum_kernel(self) -> Optional[QuantumKernel]:
+        """Return the optimized quantum kernel object."""
+        return self._quantum_kernel
+
+    @quantum_kernel.setter
+    def quantum_kernel(self, quantum_kernel: QuantumKernel) -> None:
+        self._quantum_kernel = quantum_kernel
+
+
+class QuantumKernelTrainer:
+    """
+    Quantum Kernel Trainer.
+    This class provides utility to train ``QuantumKernel`` feature map parameters.
+
+    **Example**
+
+    .. code-block::
+
+        # Create 2-qubit feature map
+        qc = QuantumCircuit(2)
+
+        # Vectors of input and trainable user parameters
+        input_params = ParameterVector("x_par", 2)
+        user_params = ParameterVector("θ_par", 2)
+
+        # Create an initial rotation layer of trainable parameters
+        for i, param in enumerate(user_params):
+            qc.ry(param, qc.qubits[i])
+
+        # Create a rotation layer of input parameters
+        for i, param in enumerate(input_params):
+            qc.rz(param, qc.qubits[i])
+
+        quant_kernel = QuantumKernel(
+            feature_map=qc,
+            user_parameters=user_params,
+            quantum_instance=...
+        )
+
+        loss_func = ...
+        optimizer = ...
+        initial_point = ...
+
+        qk_trainer = QuantumKernelTrainer(
+                                        quantum_kernel=quant_kernel,
+                                        loss=loss_func,
+                                        optimizer=optimizer,
+                                        initial_point=initial_point,
+                                        )
+        qkt_results = qk_trainer.fit_kernel(X_train, y_train)
+        optimized_kernel = qkt_results.quantum_kernel
+    """
+
+    def __init__(
+        self,
+        quantum_kernel: QuantumKernel,
+        loss: Union[str, Callable[[Sequence[float]], float]] = "svc_loss",
+        optimizer: Optional[Optimizer] = None,
+        initial_point: Optional[Sequence[float]] = None,
+    ):
+        """
+        Args:
+            quantum_kernel: QuantumKernel to be trained
+            loss (Callable[[Sequence[float]], float] or str):
+                str: Loss functions available via string: {'svc_loss: SVCLoss()).
+                    If a string is passed as the loss function, then the underlying
+                    KernelLoss object will exhibit default behavior.
+                Callable[[Sequence[float]], float]: A callable loss function which takes
+                    a vector of parameter values as input and returns a loss value (float)
+            optimizer: An instance of ``Optimizer`` to be used in training. Since no
+                analytical gradient is defined for kernel loss functions, gradient-based
+                optimizers are not recommended for training kernels.
+            initial_point: Initial point from which the optimizer will begin.
+
+        Raises:
+            ValueError: unknown loss function
+        """
+        # Class fields
+        self._quantum_kernel = quantum_kernel
+        self._initial_point = initial_point
+        self._optimizer = optimizer if optimizer else SPSA()
+
+        # Set up loss function
+        if isinstance(loss, str):
+            loss = loss.lower()
+            if loss == "svc_loss":
+                self._loss = self._str_to_loss(loss)
+            else:
+                raise ValueError(f"Unknown loss {loss}!")
+        elif isinstance(loss, KernelLoss):
+            self._loss = loss
+        else:
+            raise ValueError(f"Unknown loss {loss}!")
+
+    @property
+    def quantum_kernel(self) -> QuantumKernel:
+        """Return the quantum kernel object."""
+        return self._quantum_kernel
+
+    @quantum_kernel.setter
+    def quantum_kernel(self, quantum_kernel: QuantumKernel) -> None:
+        """Set the quantum kernel."""
+        self._quantum_kernel = quantum_kernel
+
+    @property
+    def loss(self) -> KernelLoss:
+        """Return the loss object."""
+        return self._loss
+
+    @loss.setter
+    def loss(self, loss: KernelLoss) -> None:
+        """Set the loss."""
+        self._loss = loss
+
+    @property
+    def optimizer(self) -> Optimizer:
+        """Return an optimizer to be used in training."""
+        return self._optimizer
+
+    @optimizer.setter
+    def optimizer(self, optimizer: Optimizer) -> None:
+        """Set the optimizer."""
+        self._optimizer = optimizer
+
+    @property
+    def initial_point(self) -> Optional[Sequence[float]]:
+        """Return initial point"""
+        return self._initial_point
+
+    @initial_point.setter
+    def initial_point(self, initial_point: Optional[Sequence[float]]) -> None:
+        """Set the initial point"""
+        self._initial_point = initial_point
+
+    def fit_kernel(
+        self,
+        data: np.ndarray,
+        labels: np.ndarray,
+    ) -> QuantumKernelTrainerResult:
+        """
+        Train the QuantumKernel by minimizing loss over the kernel parameters. The input
+        quantum kernel will not be altered, and an optimized quantum kernel will be returned.
+
+        Args:
+            data (numpy.ndarray): ``(N, D)`` array of training data, where ``N`` is the
+                              number of samples and ``D`` is the feature dimension
+            labels (numpy.ndarray): ``(N, 1)`` array of target values for the training samples
+
+        Returns:
+            QuantumKernelTrainerResult: the results of kernel training
+
+        Raises:
+            ValueError: No trainable user parameters specified in quantum kernel
+        """
+        # Number of parameters to tune
+        num_params = len(self._quantum_kernel.user_parameters)
+        if num_params == 0:
+            msg = "Quantum kernel cannot be fit because there are no user parameters specified."
+            raise ValueError(msg)
+
+        # Bind inputs to objective function
+        output_kernel = copy.deepcopy(self._quantum_kernel)
+
+        # Randomly initialize the initial point if one was not passed
+        if self._initial_point is None:
+            self._initial_point = algorithm_globals.random.random(num_params)
+
+        # Perform kernel optimization
+        loss_function = partial(
+            self._loss.evaluate, quantum_kernel=self.quantum_kernel, data=data, labels=labels
+        )
+        opt_results = self._optimizer.minimize(
+            fun=loss_function,
+            x0=self._initial_point,
+        )
+
+        # Return kernel training results
+        result = QuantumKernelTrainerResult()
+        result.optimizer_evals = opt_results.nfev
+        result.optimal_value = opt_results.fun
+        result.optimal_point = opt_results.x
+        result.optimal_parameters = dict(zip(output_kernel.user_parameters, opt_results.x))
+
+        # Return the QuantumKernel in optimized state
+        output_kernel.assign_user_parameters(result.optimal_parameters)
+        result.quantum_kernel = output_kernel
+
+        return result
+
+    def _str_to_loss(self, loss_str: str) -> KernelLoss:
+        if loss_str == "svc_loss":
+            loss_obj = SVCLoss()
+        else:
+            raise ValueError(f"Unknown loss {loss_str}!")
+
+        return loss_obj