The Machine Learning package simply contains sample datasets at present. It has some classification algorithms such as QSVM and VQC (Variational Quantum Classifier), where this data can be used for experiments, and there is also QGAN (Quantum Generative Adversarial Network) algorithm.
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 import BasicAer
from qiskit.utils import QuantumInstance, algorithm_globals
from qiskit.algorithms.optimizers import COBYLA
from qiskit.circuit.library import TwoLocal
from qiskit_machine_learning.algorithms import VQC
from qiskit_machine_learning.datasets import wine
from qiskit_machine_learning.circuit.library import RawFeatureVector
seed = 1376
algorithm_globals.random_seed = seed
# Use Wine data set for training and test data
feature_dim = 4 # dimension of each data point
training_size = 12
test_size = 4
# training features, training labels, test features, test labels as np.array,
# one hot encoding for labels
training_features, training_labels, test_features, test_labels = \
wine(training_size=training_size, test_size=test_size, n=feature_dim)
feature_map = RawFeatureVector(feature_dimension=feature_dim)
ansatz = TwoLocal(feature_map.num_qubits, ['ry', 'rz'], 'cz', reps=3)
vqc = VQC(feature_map=feature_map,
ansatz=ansatz,
optimizer=COBYLA(maxiter=100),
quantum_instance=QuantumInstance(BasicAer.get_backend('statevector_simulator'),
shots=1024,
seed_simulator=seed,
seed_transpiler=seed)
)
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.
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.