Qiskit-machine-learning

Changelog

Bug Fixes

- Compatibility fix to support Python 3.11.

- The function `qiskit_machine_learning.datasets.discretize_and_truncate()` is fixed on numpy version 1.24. This function is used by the QGAN implementation.

Changelog

New Features

- Allow callable as an optimizer in NeuralNetworkClassifier, VQC, NeuralNetworkRegressor, VQR, as well as in [QuantumKernelTrainer](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.kernels.algorithms.QuantumKernelTrainer.html#qiskit_machine_learning.kernels.algorithms.QuantumKernelTrainer).

Now, the optimizer can either be one of Qiskit’s optimizers, such as [SPSA](https://qiskit.org/documentation/stubs/qiskit.algorithms.optimizers.SPSA.html#qiskit.algorithms.optimizers.SPSA) or a callable with the following signature:

python
from qiskit.algorithms.optimizers import OptimizerResult

def my_optimizer(fun, x0, jac=None, bounds=None) -> OptimizerResult:
Args:
fun (callable): the function to minimize
x0 (np.ndarray): the initial point for the optimization
jac (callable, optional): the gradient of the objective function
bounds (list, optional): a list of tuples specifying the parameter bounds
result = OptimizerResult()
result.x = optimal parameters
result.fun = optimal function value
return result

      The above signature also allows to directly pass any SciPy minimizer, for instance as

python
from functools import partial
from scipy.optimize import minimize
optimizer = partial(minimize, method="L-BFGS-B")


- Added a new `FidelityStatevectorKernel` class that is optimized to use only statevector-implemented feature maps. Therefore, computational complexity is reduced from $O(N^2)$ to $O(N)$.

Computed statevector arrays are also cached to further increase efficiency. This cache is cleared when the `evaluate` method is called, unless `auto_clear_cache` is `False`. The cache is unbounded by default, but its size can be set by the user, i.e., limited to the number of samples in the worst case.

By default the Terra reference `Statevector` is used, however, the type can be specified via the `statevector_type` argument.

Shot noise emulation can also be added. If `shots` is `None`, the exact fidelity is used. Otherwise, the mean is taken of samples drawn from a binomial distribution with probability equal to the exact fidelity.

With the addition of shot noise, the kernel matrix may no longer be positive semi-definite (PSD). With `enforce_psd` set to `True` this condition is enforced.

An example of using this class is as follows:

python
from sklearn.datasets import make_blobs
from sklearn.svm import SVC

from qiskit.circuit.library import ZZFeatureMap
from qiskit.quantum_info import Statevector

from qiskit_machine_learning.kernels import FidelityStatevectorKernel

generate a simple dataset
features, labels = make_blobs(
n_samples=20, centers=2, center_box=(-1, 1), cluster_std=0.1
)

feature_map = ZZFeatureMap(feature_dimension=2, reps=2)
statevector_type = Statevector

kernel = FidelityStatevectorKernel(
feature_map=feature_map,
statevector_type=Statevector,
cache_size=len(labels),
auto_clear_cache=True,
shots=1000,
enforce_psd=True,
)
svc = SVC(kernel=kernel.evaluate)
svc.fit(features, labels)


- The PyTorch connector `TorchConnector` now fully supports sparse output in both forward and backward passes. To enable sparse support, first of all, the underlying quantum neural network must be sparse. In this case, if the sparse property of the connector itself is not set, then the connector inherits sparsity from the networks. If the connector is set to be sparse, but the network is not, an exception will be raised. Also you may set the connector to be dense if the network is sparse.

This snippet illustrates how to create a sparse instance of the connector.

python
import torch
from qiskit import QuantumCircuit
from qiskit.circuit.library import ZFeatureMap, RealAmplitudes

from qiskit_machine_learning.connectors import TorchConnector
from qiskit_machine_learning.neural_networks import SamplerQNN

num_qubits = 2
fmap = ZFeatureMap(num_qubits, reps=1)
ansatz = RealAmplitudes(num_qubits, reps=1)
qc = QuantumCircuit(num_qubits)
qc.compose(fmap, inplace=True)
qc.compose(ansatz, inplace=True)

qnn = SamplerQNN(
circuit=qc,
input_params=fmap.parameters,
weight_params=ansatz.parameters,
sparse=True,
)

connector = TorchConnector(qnn)

output = connector(torch.tensor([[1., 2.]]))
print(output)

loss = torch.sparse.sum(output)
loss.backward()

grad = connector.weight.grad
print(grad)


      In hybrid setup, where a PyTorch-based neural network has classical and quantum layers, sparse operations should not be mixed with dense ones, otherwise exceptions may be thrown by PyTorch.

      Sparse support works on python 3.8+.

Upgrade Notes

- The previously deprecated `CrossEntropySigmoidLoss` loss function has been removed.
- The previously deprecated datasets have been removed: `breast_cancer`, `digits`, `gaussian`, `iris`, `wine`.
- Positional arguments in [`QSVC`](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.algorithms.QSVC.html#qiskit_machine_learning.algorithms.QSVC) and [`QSVR`](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.algorithms.QSVR.html#qiskit_machine_learning.algorithms.QSVR) were deprecated as of version 0.3. Support of the positional arguments was completely removed in this version, please replace them with corresponding keyword arguments.

Bug Fixes
- [`SamplerQNN`](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.neural_networks.SamplerQNN.html#qiskit_machine_learning.neural_networks.SamplerQNN) can now correctly handle quantum circuits without both input parameters and weights. If such a circuit is passed to the QNN then this circuit executed once in the forward pass and backward returns `None` for both gradients.

Changelog

New Features

- Added support for categorical and ordinal labels to [VQC](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.algorithms.VQC.html#qiskit_machine_learning.algorithms.VQC). Now labels can be passed in different formats, they can be plain ordinal labels, a one dimensional array that contains integer labels like 0, 1, 2, …, or an array with categorical string labels. One-hot encoded labels are still supported. Internally, labels are transformed to one hot encoding and the classifier is always trained on one hot labels.
- Introduced Estimator Quantum Neural Network ([EstimatorQNN](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.neural_networks.EstimatorQNN.html#qiskit_machine_learning.neural_networks.EstimatorQNN)) based on (runtime) primitives. This implementation leverages the estimator primitive (see [BaseEstimator](https://qiskit.org/documentation/stubs/qiskit.primitives.BaseEstimator.html#qiskit.primitives.BaseEstimator)) and the estimator gradients (see [BaseEstimatorGradient](https://qiskit.org/documentation/stubs/qiskit.algorithms.gradients.BaseEstimatorGradient.html#qiskit.algorithms.gradients.BaseEstimatorGradient)) to enable runtime access and more efficient computation of forward and backward passes.
The new [EstimatorQNN](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.neural_networks.EstimatorQNN.html#qiskit_machine_learning.neural_networks.EstimatorQNN) exposes a similar interface to the Opflow QNN, with a few differences. One is the quantum_instance parameter. This parameter does not have a direct replacement, and instead the estimator parameter must be used. The gradient parameter keeps the same name as in the Opflow QNN implementation, but it no longer accepts Opflow gradient classes as inputs; instead, this parameter expects an (optionally custom) primitive gradient.
The existing training algorithms such as [VQR](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.algorithms.VQR.html#qiskit_machine_learning.algorithms.VQR), that were based on the Opflow QNN, are updated to accept both implementations. The implementation of [NeuralNetworkRegressor](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.algorithms.NeuralNetworkRegressor.html#qiskit_machine_learning.algorithms.NeuralNetworkRegressor) has not changed.

- Introduced Quantum Kernels based on (runtime) primitives. This implementation leverages the fidelity primitive (see [BaseStateFidelity](https://qiskit.org/documentation/stubs/qiskit.algorithms.state_fidelities.BaseStateFidelity.html#qiskit.algorithms.state_fidelities.BaseStateFidelity)) and provides more flexibility to end users. The fidelity primitive calculates state fidelities/overlaps for pairs of quantum circuits and requires an instance of [Sampler](https://qiskit.org/documentation/stubs/qiskit.primitives.Sampler.html#qiskit.primitives.Sampler). Thus, users may plug in their own implementations of fidelity calculations.
The new kernels expose the same interface and the same parameters except the quantum_instance parameter. This parameter does not have a direct replacement and instead the fidelity parameter must be used.

A new hierarchy is introduced:

- A base and abstract class [BaseKernel](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.kernels.BaseKernel.html#qiskit_machine_learning.kernels.BaseKernel) is introduced. All concrete implementation must inherit this class.

- A fidelity based quantum kernel [FidelityQuantumKernel](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.kernels.FidelityQuantumKernel.html#qiskit_machine_learning.kernels.FidelityQuantumKernel) is added. This is a direct replacement of [QuantumKernel](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.kernels.QuantumKernel.html#qiskit_machine_learning.kernels.QuantumKernel). The difference is that the new class takes either a sampler or a fidelity instance to estimate overlaps and construct kernel matrix.

- A new abstract class [TrainableKernel](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.kernels.TrainableKernel.html#qiskit_machine_learning.kernels.TrainableKernel) is introduced to generalize ability to train quantum kernels.

- A fidelity-based trainable quantum kernel [TrainableFidelityQuantumKernel](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.kernels.TrainableFidelityQuantumKernel.html#qiskit_machine_learning.kernels.TrainableFidelityQuantumKernel) is introduced. This is a replacement of the existing [QuantumKernel](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.kernels.QuantumKernel.html#qiskit_machine_learning.kernels.QuantumKernel) if a trainable kernel is required. The trainer [QuantumKernelTrainer](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.kernels.algorithms.QuantumKernelTrainer.html#qiskit_machine_learning.kernels.algorithms.QuantumKernelTrainer) now accepts both quantum kernel implementations, the new one and the existing one.

The existing algorithms such as [QSVC](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.algorithms.QSVC.html#qiskit_machine_learning.algorithms.QSVC), [QSVR](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.algorithms.QSVR.html#qiskit_machine_learning.algorithms.QSVR) and other kernel-based algorithms are updated to accept both implementations.

- Introduced Sampler Quantum Neural Network ([SamplerQNN](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.neural_networks.SamplerQNN.html#qiskit_machine_learning.neural_networks.SamplerQNN)) based on (runtime) primitives. This implementation leverages the sampler primitive (see [BaseSampler](https://qiskit.org/documentation/stubs/qiskit.primitives.BaseSampler.html#qiskit.primitives.BaseSampler)) and the sampler gradients (see [BaseSamplerGradient](https://qiskit.org/documentation/stubs/qiskit.algorithms.gradients.BaseSamplerGradient.html#qiskit.algorithms.gradients.BaseSamplerGradient)) to enable runtime access and more efficient computation of forward and backward passes more efficiently.
The new [SamplerQNN](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.neural_networks.SamplerQNN.html#qiskit_machine_learning.neural_networks.SamplerQNN) exposes a similar interface to the [CircuitQNN](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.neural_networks.CircuitQNN.html#qiskit_machine_learning.neural_networks.CircuitQNN), with a few differences. One is the quantum_instance parameter. This parameter does not have a direct replacement, and instead the sampler parameter must be used. The gradient parameter keeps the same name as in the [CircuitQNN](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.neural_networks.CircuitQNN.html#qiskit_machine_learning.neural_networks.CircuitQNN) implementation, but it no longer accepts Opflow gradient classes as inputs; instead, this parameter expects an (optionally custom) primitive gradient. The sampling option has been removed for the time being, as this information is not currently exposed by the Sampler, and might correspond to future lower-level primitives.

- The existing training algorithms such as [VQC](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.algorithms.VQC.html#qiskit_machine_learning.algorithms.VQC), that were based on the [CircuitQNN](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.neural_networks.CircuitQNN.html#qiskit_machine_learning.neural_networks.CircuitQNN), are updated to accept both implementations. The implementation of [NeuralNetworkClassifier](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.algorithms.NeuralNetworkClassifier.html#qiskit_machine_learning.algorithms.NeuralNetworkClassifier) has not changed.
- Expose the callback attribute as public property on [TrainableModel](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.algorithms.TrainableModel.html#qiskit_machine_learning.algorithms.TrainableModel). This, for instance, allows setting the callback between optimizations and store the history in separate objects.
- Gradient operator/circuit initialization in [OpflowQNN](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.neural_networks.OpflowQNN.html#qiskit_machine_learning.neural_networks.OpflowQNN) and [CircuitQNN](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.neural_networks.CircuitQNN.html#qiskit_machine_learning.neural_networks.CircuitQNN) respectively is now delayed until the first call of the backward method. Thus, the networks are created faster and gradient framework objects are not created until they are required.
- Introduced a new parameter evaluate_duplicates in [QuantumKernel](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.kernels.QuantumKernel.html#qiskit_machine_learning.kernels.QuantumKernel). This parameter defines a strategy how kernel matrix elements are evaluated if duplicate samples are found. Possible values are:

- all means that all kernel matrix elements are evaluated, even the diagonal ones when

training. This may introduce additional noise in the matrix.

- off_diagonal when training the matrix diagonal is set to 1, the rest elements are

fully evaluated, e.g., for two identical samples in the dataset. When inferring, all elements are evaluated. This is the default value.

- none when training the diagonal is set to 1 and if two identical samples are found

in the dataset the corresponding matrix element is set to 1. When inferring, matrix elements for identical samples are set to 1.

- In the previous releases, in the QGAN class, the gradient penalty could not be enabled to train the discriminator with a penalty function. Thus, a gradient penalty parameter was added during the initialization of the QGAN algorithm. This parameter indicates whether or not penalty function is applied to the loss function of the discriminator during training.

- Enable the default construction of the ZFeatureMap in the [TwoLayerQNN](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.neural_networks.TwoLayerQNN.html#qiskit_machine_learning.neural_networks.TwoLayerQNN) if the number of qubits is 1. Previously, not providing a feature map for the single qubit case raised an error as default construction assumed 2 or more qubits.

- [VQC](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.algorithms.VQC.html#qiskit_machine_learning.algorithms.VQC) will now raise an error when training from a warm start when encountering targets with a different number of classes to the previous dataset.

- [VQC](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.algorithms.VQC.html#qiskit_machine_learning.algorithms.VQC) will now raise an error when a user attempts multi-label classification, which is not supported.

- Added two new properties to the [TrainableModel](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.algorithms.TrainableModel.html#qiskit_machine_learning.algorithms.TrainableModel) class:

- fit_result returns a resulting object from the optimization procedure. Please refers to the Terra’s documentation of the OptimizerResult class.
- weights returns an array of trained weights, this is a convenient way to get access to the weights, it is the same as calling model.fit_result.x.

Upgrade Notes

- The method [fit()](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.algorithms.TrainableModel.fit.html#qiskit_machine_learning.algorithms.TrainableModel.fit) is not abstract any more. Now, it implements basic checks, calls a new abstract method _fit_internal() to be implemented by sub-classes, and keeps track of [fit_result](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.algorithms.TrainableModel.fit_result.html#qiskit_machine_learning.algorithms.TrainableModel.fit_result) property that is returned by this new abstract method. Thus, any sub-class of [TrainableModel](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.algorithms.TrainableModel.html#qiskit_machine_learning.algorithms.TrainableModel) must implement this new method. Classes [NeuralNetworkClassifier](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.algorithms.NeuralNetworkClassifier.html#qiskit_machine_learning.algorithms.NeuralNetworkClassifier) and [NeuralNetworkRegressor](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.algorithms.NeuralNetworkRegressor.html#qiskit_machine_learning.algorithms.NeuralNetworkRegressor) have been updated correspondingly.

- Inheriting from sklearn.svm.SVC in PegasosQSVC resulted in errors when calling some inherited methods such as decision_function due to the overridden fit implementation. For that reason, the inheritance has been replaced by a much lighter inheritance from ClassifierMixin providing the score method and a new method decision_function has been implemented. The class is still sklearn compatible due to duck typing. This means that for the user everything that has been working in the previous release still works, except the inheritance. The only methods that are no longer supported (such as predict_proba) were only raising errors in the previous release in practice.

Deprecation Notes

- The qiskit_machine_learning.algorithms.distribution_learners package is deprecated and will be removed no sooner than 3 months after the release. There’s no direct replacement for the classes from this package. Instead, please refer to the new QGAN tutorial. This tutorial introduces step-by-step how to build a PyTorch-based QGAN using quantum neural networks.

- Classes [qiskit_machine_learning.runtime.TorchRuntimeClient](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.runtime.TorchRuntimeClient.html#qiskit_machine_learning.runtime.TorchRuntimeClient), [qiskit_machine_learning.runtime.TorchRuntimeResult](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.runtime.TorchRuntimeResult.html#qiskit_machine_learning.runtime.TorchRuntimeResult), qiskit_machine_learning.runtime.HookBase and functions qiskit_machine_learning.runtime.str_to_obj(), qiskit_machine_learning.runtime.obj_to_str() are being deprecated. You should use QiskitRuntimeService to leverage primitives and runtimes.

- Class [qiskit_machine_learning.neural_networks.CircuitQNN](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.neural_networks.CircuitQNN.html#qiskit_machine_learning.neural_networks.CircuitQNN) is pending deprecation and is superseded by [qiskit_machine_learning.neural_networks.SamplerQNN](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.neural_networks.SamplerQNN.html#qiskit_machine_learning.neural_networks.SamplerQNN).

- Class [qiskit_machine_learning.neural_networks.OpflowQNN](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.neural_networks.OpflowQNN.html#qiskit_machine_learning.neural_networks.OpflowQNN) is pending deprecation and is superseded by [qiskit_machine_learning.neural_networks.EstimatorQNN](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.neural_networks.EstimatorQNN.html#qiskit_machine_learning.neural_networks.EstimatorQNN).

- Class [qiskit_machine_learning.neural_networks.TwoLayerQNN](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.neural_networks.TwoLayerQNN.html#qiskit_machine_learning.neural_networks.TwoLayerQNN) is pending deprecation and has no direct replacement. Please make use of [qiskit_machine_learning.neural_networks.EstimatorQNN](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.neural_networks.EstimatorQNN.html#qiskit_machine_learning.neural_networks.EstimatorQNN) instead.

These classes will be deprecated in a future release and subsequently removed after that.

Class [qiskit_machine_learning.kernels.QuantumKernel](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.kernels.QuantumKernel.html#qiskit_machine_learning.kernels.QuantumKernel) is pending deprecation and is superseded by [qiskit_machine_learning.kernels.FidelityQuantumKernel](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.kernels.FidelityQuantumKernel.html#qiskit_machine_learning.kernels.FidelityQuantumKernel) and [qiskit_machine_learning.kernels.TrainableFidelityQuantumKernel](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.kernels.TrainableFidelityQuantumKernel.html#qiskit_machine_learning.kernels.TrainableFidelityQuantumKernel).

This class will be deprecated in a future release and subsequently removed after that.

- For the class [QuantumKernel](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.kernels.QuantumKernel.html#qiskit_machine_learning.kernels.QuantumKernel), to improve usability and better describe the usage, user_parameters has been renamed to training_parameters; current behavior is retained. For this change the constructor parameter user_parameters is now deprecated and replaced by training_parameters. The related properties and methods are renamed to match. That is to say:

- argument user_parameters -> training_parameters

- property user_parameters -> training_parameters

- property user_param_binds -> training_parameter_binds

- method assign_user_parameters -> assign_training_parameters

- method bind_user_parameters -> bind_training_parameters

- method get_unbound_user_parameters -> get_unbound_training_parameters

Bug Fixes

- Previously in the [QuantumGenerator](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.algorithms.QuantumGenerator.html#qiskit_machine_learning.algorithms.QuantumGenerator) of the [QGAN](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.algorithms.QGAN.html#qiskit_machine_learning.algorithms.QGAN) algorithm, if we used a simulator other than the statevector_simulator the result dictionary had not the correct size to compute both the gradient and the loss functions. Now, the values output are stored in a vector of size 2^n and each key is mapped to its value from the result dictionary in the new value array. Also, each key is stored in a vector of size 2^n where each element of the vector keys[i] corresponds to the binary representation of i.

- Previously in the [QuantumGenerator](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.algorithms.QuantumGenerator.html#qiskit_machine_learning.algorithms.QuantumGenerator) of the [QGAN](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.algorithms.QGAN.html#qiskit_machine_learning.algorithms.QGAN) algorithm, the gradients were computed using the statevector backend even if we specified another backend. To solve this issue, the gradient object is converted into a CircuitStateFn instead of its adjoint as in the previous version. The gradients are converted into the backend-dependent structure using CircuitSampler. After the evaluation of the object, the gradient_function is stored in a dense array to fix a dimension incompatibility when computing the loss function.

- Fixed quantum kernel evaluation when duplicate samples are found in the dataset. Originally, kernel matrix elements were not evaluated for identical samples in the dataset and such elements were set wrongly to zero. Now we introduced a new parameter evaluate_duplicates that ensures that elements of the kernel matrix are evaluated correctly. See the feature section for more details.

- Previously in the pytorch_discriminator class of the QGAN algorithm, if the gradient penalty parameter was enabled, the latent variable z was not properly initialized : Variable module was used instead of torch.autograd.Variable.

- Calling PegasosQSVC.decision_function() raises an error. Fixed by writing own method instead of inheriting from SVC. The inheritance from SVC in the PegasosQSVC class is removed. To keep the score method, inheritance to the mixin class ClassifierMixin from scikit-learn is added.

Changelog

New Features

- In the previous releases at the backpropagation stage of CircuitQNN and OpflowQNN gradients were computed for each sample in a dataset individually and then the obtained values were aggregated into one output array. Thus, for each sample in a dataset at least one job was submitted. Now, gradients are computed for all samples in a dataset in one go by passing a list of values for a single parameter to CircuitSampler. Therefore, a number of jobs required for such computations is significantly reduced. This improvement may speed up training process in the cloud environment, where queue time for submitting a job may be a major contribution in the overall training time.

- Introduced two new classes, [EffectiveDimension](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.neural_networks.EffectiveDimension.html#qiskit_machine_learning.neural_networks.EffectiveDimension) and LocalEffectiveDimension, for calculating the capacity of quantum neural network models through the computation of the Fisher Information Matrix. The local effective dimension bounds the generalization error of QNNs and only accepts single parameter sets as inputs. The global effective dimension (or just effective dimension) can be used as a measure of the expressibility of the model, and accepts multiple parameter sets.

- Objective functions constructed by the neural network classifiers and regressors now include an averaging factor that is evaluated as 1 / number_of_samples. Computed averaged objective values are passed to a user specified callback if any. Users may notice a dramatic decrease in the objective values in their callbacks. This is due to this averaging factor.

- Added support for saving and loading machine learning models. This support is introduced in TrainableModel, so all sub-classes can be saved and loaded. Also, kernel based models can be saved and loaded. A list of models that support saving and loading models:

NeuralNetworkClassifier

NeuralNetworkRegressor

VQC

VQR

QSVC

QSVR

PegasosQSVC

- When model is saved all model parameters are saved to a file, including a quantum instance that is referenced by internal objects. That means if a model is loaded from a file and is used, for instance, for inference, the same quantum instance and a corresponding backend will be used even if a cloud backend was used.

- Added a new feature in CircuitQNN that ensures unbound_pass_manager is called when caching the QNN circuit and that bound_pass_manager is called when QNN parameters are assigned.

- Added a new feature in QuantumKernel that ensures the bound_pass_manager is used, when provided via the QuantumInstance, when transpiling the kernel circuits.

Upgrade Notes

- Added support for running with Python 3.10. At the the time of the release, Torch didn’t have a python 3.10 version.

- The previously deprecated BaseBackend class has been removed. It was originally deprecated in the Qiskit Terra 0.18.0 release.

- Support for running with Python 3.6 has been removed. To run Machine Learning you need a minimum Python version of 3.7.

Deprecation Notes

- The functions breast_cancer, digits, gaussian, iris and wine in the datasets module are deprecated and should not be used.

- Class CrossEntropySigmoidLoss is deprecated and marked for removal.

- Removed support of l2 and l1 values as loss function definitions. Please, use absolute_error and squared_error respectively.

Bug Fixes

- Fixes in Ad Hoc dataset. Fixed an ValueError when n=3 is passed to ad_hoc_data. When the value of n is not 2 or 3, a ValueError is raised with a message that the only supported values of n are 2 and 3.

- Previously, VQC would throw an error if trained on batches of data where not all of the target labels that can be found in the full dataset were present. This is because VQC interpreted the number of unique targets in the current batch as the number of classes. Currently, VQC is hard-coded to expect one-hot-encoded targets. Therefore, VQC will now determine the number of classes from the shape of the target array.

- Fixes an issue where VQC could not be trained on multiclass datasets. It returned nan values on some iterations. This is fixed in 2 ways. First, the default parity function is now guaranteed to be able to assign at least one output bitstring to each class, so long as 2**N >= C where N is the number of output qubits and C is the number of classes. This guarantees that it is at least possible for every class to be predicted with a non-zero probability. Second, even with this change it is still possible that on a given training instance a class is predicted with 0 probability. Previously this could lead to nan in the CrossEntropyLoss calculation. We now replace 0 probabilities with a small positive value to ensure the loss cannot return nan.

- Fixes an issue in QuantumKernel where evaluating a quantum kernel for data with dimension d>2 raised an error. This is fixed by changing the hard-coded reshaping of one-dimensional arrays in QuantumKernel.evaluate().

- Fixes an issue where VQC would fail with warm_start=True. The extraction of the initial_point in TrainableModel from the final point of the minimization had not been updated to reflect the refactor of optimizers in qiskit-terra; the old optimize method, that returned a tuple was deprecated and new method minimize was created that returns an OptimizerResult object. We now correctly recover the final point of the minimization from previous fits to use for a warm start in subsequent fits.

- Added GPU support to TorchConnector. Now, if a hybrid PyTorch model is being trained on GPU, TorchConnector correctly detaches tensors, moves them to CPU, evaluate forward and backward passes and places resulting tensors to the same device they came from.

- Fixed a bug when a sparse array is passed to VQC as labels. Sparse arrays can be easily observed when labels are encoded via OneHotEncoder from SciKit-Learn. Now both NeuralNetworkClassifier and VQC support sparse arrays and convert them dense arrays in the implementation.

Changelog

Upgrade Notes

- Added support for running with Python 3.10. At the the time of the release, Torch didn’t have a python 3.10 version.

Bug Fixes

- Fixes in Ad Hoc dataset. Fixed an ValueError when n=3 is passed to ad_hoc_data. When the value of n is not 2 or 3, a ValueError is raised with a message that the only supported values of n are 2 and 3.

- Previously, VQC would throw an error if trained on batches of data where not all of the target labels that can be found in the full dataset were present. This is because VQC interpreted the number of unique targets in the current batch as the number of classes. Currently, VQC is hard-coded to expect one-hot-encoded targets. Therefore, VQC will now determine the number of classes from the shape of the target array.

- Fixes an issue where VQC could not be trained on multiclass datasets. It returned nan values on some iterations. This is fixed in 2 ways. First, the default parity function is now guaranteed to be able to assign at least one output bitstring to each class, so long as 2**N >= C where N is the number of output qubits and C is the number of classes. This guarantees that it is at least possible for every class to be predicted with a non-zero probability. Second, even with this change it is still possible that on a given training instance a class is predicted with 0 probability. Previously this could lead to nan in the CrossEntropyLoss calculation. We now replace 0 probabilities with a small positive value to ensure the loss cannot return nan.

- Fixes an issue where VQC would fail with warm_start=True. The extraction of the initial_point in TrainableModel from the final point of the minimization had not been updated to reflect the refactor of optimizers in qiskit-terra; the old optimize method, that returned a tuple was deprecated and new method minimize was created that returns an OptimizerResult object. We now correctly recover the final point of the minimization from previous fits to use for a warm start in subsequent fits.

Changelog

New Features

- Addition of a QuantumKernelTrainer object which may be used by kernel-based machine learning algorithms to perform optimization of some QuantumKernel parameters before training the model. Addition of a new base class, KernelLoss, in the loss_functions package. Addition of a new KernelLoss subclass, SVCLoss.

- The class TrainableModel, and its sub-classes NeuralNetworkClassifier, NeuralNetworkRegressor, VQR, VQC, have a new optional argument callback. User can optionally provide a callback function that can access the intermediate training data to track the optimization process, else it defaults to None. The callback function takes in two parameters: the weights for the objective function and the computed objective value. For each iteration an optimizer invokes the callback and passes current weights and computed value of the objective function.

- Classification models (i.e. models that extend the NeuralNetworkClassifier class like VQC) can now handle categorical target data in methods like fit() and score(). Categorical data is inferred from the presence of string type data and is automatically encoded using either one-hot or integer encodings. Encoder type is determined by the one_hot argument supplied when instantiating the model.

- There’s an additional transpilation step introduced in CircuitQNN that is invoked when a quantum instance is set. A circuit passed to CircuitQNN is transpiled and saved for subsequent usages. So, every time when the circuit is executed it is already transpiled and overall time of the forward pass is reduced. Due to implementation limitations of RawFeatureVector it can’t be transpiled in advance, so it is transpiled every time it is required to be executed and only when all parameters are bound. This means overall performance when RawFeatureVector is used stays the same.

- Introduced a new classification algorithm, which is an alternative version of the Quantum Support Vector Classifier (QSVC) that is trained via the Pegasos algorithm from https://home.ttic.edu/~nati/Publications/PegasosMPB.pdf instead of the dual optimization problem like in sklearn. This algorithm yields a training complexity that is independent of the size of the training set (see the to be published Master’s Thesis “Comparing Quantum Neural Networks and Quantum Support Vector Machines” by Arne Thomsen), such that the PegasosQSVC is expected to train faster than QSVC for sufficiently large training sets.

- QuantumKernel transpiles all circuits before execution. However, this
information was not being passed, which calls the transpiler many times during the execution of the QSVC/QSVR algorithm. Now, had_transpiled=True is passed correctly and the algorithm runs faster.

- QuantumKernel now provides an interface for users to specify a new class field, user_parameters. User parameters are an array of Parameter objects corresponding to parameterized quantum gates in the feature map circuit the user wishes to tune. This is useful in algorithms where feature map parameters must be bound and re-bound many times (i.e. variational algorithms). Users may also use a new function assign_user_parameters to assign real values to some or all of the user parameters in the feature map.

- Introduce the TorchRuntimeClient for training a quantum model or a hybrid quantum-classical model faster using Qiskit Runtime. It can also be used for predicting the result using the trained model or calculating the score of the trained model faster using Qiskit Runtime.

Known Issues

- If positional arguments are passed into QSVR or QSVC and these classes are printed, an exception is raised.

Deprecation Notes

- Positional arguments in QSVR and QSVC are deprecated.

Bug Fixes

- Fixed a bug in QuantumKernel where for statevector simulator all circuits were constructed and transpiled at once, leading to high memory usage. Now the circuits are batched similarly to how it was previously done for non-statevector simulators (same flag is used for both now; previously batch_size was silently ignored by statevector simulator)

- Fix a bug where TorchConnector failed on backward pass computation due to empty parameters for inputs or weights. Validation added to qiskit_machine_learning.neural_networks.NeuralNetwork._validate_backward_output().

- TwoLayerQNN now passes the value of the exp_val parameter in the constructor to the constructor of OpflowNN which TwoLayerQNN inherits from.

- In some configurations forward pass of a neural network may return the same value across multiple calls even if different weights are passed. This behavior is confirmed with AQGD optimizer. This was due to a bug in the implementation of the objective functions. They cache a value obtained at the forward pass to be re-used in the backward pass. Initially, this cache was based on an identifier (a call of id() function) of the weights array. AQGD re-uses the same array for weights: it updates the values keeping an instance of the array the same. This caused to re-use the same forward pass value across all iteration. Now the forward pass cache is based on actual values of weights instead of identifiers.

- Fix a bug, where qiskit_machine_learning.circuit.library.RawFeatureVector.copy() didn’t copy all internal settings which could lead to issues with the copied circuit. As a consequence qiskit_machine_learning.circuit.library.RawFeatureVector.bind_parameters() is also fixed.

- Fixes a bug where VQC could not be instantiated unless either feature_map or ansatz were provided (217). VQC is now instantiated with the default feature_map and/or ansatz.

- The QNN weight parameter in TorchConnector is now registered in the torch DAG as weight, instead of _weights. This is consistent with the PyTorch naming convention and the weight property used to get access to the computed weights.