Qiskit Machine Learning introduces fundamental computational building blocks - such as Quantum Kernels and Quantum Neural Networks - used in different applications, including classification and regression. On the one hand, this design is very easy to use and allows users to rapidly prototype a first model without deep quantum computing knowledge. On the other hand, Qiskit Machine Learning is very flexible, and users can easily extend it to support cutting-edge quantum machine learning research.
Qiskit Machine Learning provides the FidelityQuantumKernel class that makes use of the Fidelity algorithm introduced in Qiskit and can be easily used to directly compute kernel matrices for given datasets or can be passed to a Quantum Support Vector Classifier QSVC or Quantum Support Vector Regressor QSVR to quickly start solving classification or regression problems. It also can be used with many other existing kernel-based machine learning algorithms from established classical frameworks.
Qiskit Machine Learning defines a generic interface for neural networks that is implemented by different quantum neural networks. Two core implementations are readily provided, such as the EstimatorQNN, and the SamplerQNN. The EstimatorQNN leverages the Estimator primitive from Qiskit and allows users to combine parametrized quantum circuits with quantum mechanical observables. The circuits can be constructed using, for example, building blocks from Qiskit’s circuit library, and the QNN’s output is given by the expected value of the observable. The SamplerQNN leverages another primitive introduced in Qiskit, the Sampler primitive. This neural network translates quasi-probabilities of bitstrings estimated by the primitive into a desired output. This translation step can be used to interpret a given bitstring in a particular context, e.g. translating it into a set of classes.
The neural networks include the functionality to evaluate them for a given input as well as to compute the corresponding gradients, which is important for efficient training. To train and use neural networks, Qiskit Machine Learning provides a variety of learning algorithms such as the NeuralNetworkClassifier and NeuralNetworkRegressor. Both take a QNN as input and then use it in a classification or regression context. To allow an easy start, two convenience implementations are provided - the Variational Quantum Classifier VQC as well as the Variational Quantum Regressor VQR. Both take just a feature map and an ansatz and construct the underlying QNN automatically.
In addition to the models provided directly in Qiskit Machine Learning, it has the TorchConnector, which allows users to integrate all of our quantum neural networks directly into the PyTorch open source machine learning library. Thanks to Qiskit’s gradient algorithms, this includes automatic differentiation - the overall gradients computed by PyTorch during the backpropagation take into account quantum neural networks, too. The flexible design also allows the building of connectors to other packages in the future.
We encourage installing Qiskit Machine Learning via the pip tool (a python package manager).
pip install qiskit-machine-learningpip will handle all dependencies automatically and you will always install the latest (and well-tested) version.
If you want to work on the very latest work-in-progress versions, either to try features ahead of their official release or if you want to contribute to Machine Learning, then you can install from source. To do this follow the instructions in the documentation.
-
PyTorch, may be installed either using command
pip install 'qiskit-machine-learning[torch]'to install the package or refer to PyTorch getting started. When PyTorch is installed, theTorchConnectorfacilitates its use of quantum computed networks. -
Sparse, may be installed using command
pip install 'qiskit-machine-learning[sparse]'to install the package. Sparse being installed will enable the usage of sparse arrays/tensors.
Now that Qiskit Machine Learning is installed, it's time to begin working with the Machine Learning module. Let's try an experiment using VQC (Variational Quantum Classifier) algorithm to train and test samples from a data set to see how accurately the test set can be classified.
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit.library import TwoLocal, ZZFeatureMap
from qiskit.utils import algorithm_globals
from qiskit_machine_learning.algorithms import VQC
from qiskit_machine_learning.datasets import ad_hoc_data
seed = 1376
algorithm_globals.random_seed = seed
# Use ad hoc data set for training and test data
feature_dim = 2 # dimension of each data point
training_size = 20
test_size = 10
# training features, training labels, test features, test labels as np.ndarray,
# one hot encoding for labels
training_features, training_labels, test_features, test_labels = ad_hoc_data(
training_size=training_size, test_size=test_size, n=feature_dim, gap=0.3
)
feature_map = ZZFeatureMap(feature_dimension=feature_dim, reps=2, entanglement="linear")
ansatz = TwoLocal(feature_map.num_qubits, ["ry", "rz"], "cz", reps=3)
vqc = VQC(
feature_map=feature_map,
ansatz=ansatz,
optimizer=COBYLA(maxiter=100),
)
vqc.fit(training_features, training_labels)
score = vqc.score(test_features, test_labels)
print(f"Testing accuracy: {score:0.2f}")Learning path notebooks may be found in the Machine Learning tutorials section of the documentation and are a great place to start.
Another good place to learn the fundamentals of quantum machine learning is the Quantum Machine Learning course on the Qiskit Textbook's website. The course is very convenient for beginners who are eager to learn quantum machine learning from scratch, as well as understand the background and theory behind algorithms in Qiskit Machine Learning. The course covers a variety of topics to build understanding of parameterized circuits, data encoding, variational algorithms etc., and in the end the ultimate goal of machine learning - how to build and train quantum ML models for supervised and unsupervised learning. The textbook course is complementary to the tutorials of this module, where the tutorials focus on actual Qiskit Machine Learning algorithms, the course more explains and details underlying fundamentals of quantum machine learning.
If you'd like to contribute to Qiskit, please take a look at our contribution guidelines. This project adheres to Qiskit's code of conduct. By participating, you are expected to uphold this code.
We use GitHub issues for tracking requests and bugs. Please join the Qiskit Slack community and for discussion and simple questions. For questions that are more suited for a forum, we use the Qiskit tag in Stack Overflow.
Machine Learning was inspired, authored and brought about by the collective work of a team of researchers. Machine Learning continues to grow with the help and work of many people, who contribute to the project at different levels. If you use Qiskit, please cite as per the provided BibTeX file.
Please note that if you do not like the way your name is cited in the BibTex file then consult the information found in the .mailmap file.
This project uses the Apache License 2.0.