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Quantum Machine Learning
https://github.com/qiskit-community/qiskit-machine-learning

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Quantum Machine Learning

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README 

        
# Qiskit Machine Learning



[![License](https://img.shields.io/github/license/Qiskit/qiskit-machine-learning.svg?style=popout-square)](https://opensource.org/licenses/Apache-2.0)[![Build Status](https://github.com/qiskit-community/qiskit-machine-learning/actions/workflows/main.yml/badge.svg)](https://github.com/qiskit-community/qiskit-machine-learning/actions?query=workflow%3A"Machine%20Learning%20Unit%20Tests"+branch%3Amain+event%3Apush)[![](https://img.shields.io/github/release/Qiskit/qiskit-machine-learning.svg?style=popout-square)](https://github.com/qiskit-community/qiskit-machine-learning/releases)[![](https://img.shields.io/pypi/dm/qiskit-machine-learning.svg?style=popout-square)](https://pypi.org/project/qiskit-machine-learning/)[![Coverage Status](https://coveralls.io/repos/github/Qiskit/qiskit-machine-learning/badge.svg?branch=main)](https://coveralls.io/github/Qiskit/qiskit-machine-learning?branch=main)



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](https://qiskit-community.github.io/qiskit-machine-learning/stubs/qiskit_machine_learning.kernels.QuantumKernel.html#qiskit_machine_learning.kernels.FidelityQuantumKernel)

class that makes use of the [Fidelity](https://qiskit-community.github.io/qiskit-algorithms/stubs/qiskit_algorithms.state_fidelities.BaseStateFidelity.html) algorithm introduced in Qiskit Algorithms and can be easily used

to directly compute kernel matrices for given datasets or can be passed to a Quantum Support Vector Classifier 

[QSVC](https://qiskit-community.github.io/qiskit-machine-learning/stubs/qiskit_machine_learning.algorithms.QSVC.html#qiskit_machine_learning.algorithms.QSVC) or

Quantum Support Vector Regressor

[QSVR](https://qiskit-community.github.io/qiskit-machine-learning/stubs/qiskit_machine_learning.algorithms.QSVR.html#qiskit_machine_learning.algorithms.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](https://qiskit-community.github.io/qiskit-machine-learning/stubs/qiskit_machine_learning.neural_networks.EstimatorQNN.html),

and the [SamplerQNN](https://qiskit-community.github.io/qiskit-machine-learning/stubs/qiskit_machine_learning.neural_networks.SamplerQNN.html).

The [EstimatorQNN](https://qiskit-community.github.io/qiskit-machine-learning/stubs/qiskit_machine_learning.neural_networks.EstimatorQNN.html)

leverages the [Estimator](https://docs.quantum.ibm.com/api/qiskit/qiskit.primitives.BaseEstimator) 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](https://qiskit-community.github.io/qiskit-machine-learning/stubs/qiskit_machine_learning.neural_networks.SamplerQNN.html)

leverages another primitive introduced in Qiskit, the [Sampler](https://docs.quantum.ibm.com/api/qiskit/qiskit.primitives.BaseSampler) 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](https://qiskit-community.github.io/qiskit-machine-learning/stubs/qiskit_machine_learning.algorithms.NeuralNetworkClassifier.html#qiskit_machine_learning.algorithms.NeuralNetworkClassifier)

and

[NeuralNetworkRegressor](https://qiskit-community.github.io/qiskit-machine-learning/stubs/qiskit_machine_learning.algorithms.NeuralNetworkRegressor.html#qiskit_machine_learning.algorithms.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](https://qiskit-community.github.io/qiskit-machine-learning/stubs/qiskit_machine_learning.algorithms.VQC.html#qiskit_machine_learning.algorithms.VQC)

as well as the Variational Quantum Regressor

[VQR](https://qiskit-community.github.io/qiskit-machine-learning/stubs/qiskit_machine_learning.algorithms.VQR.html#qiskit_machine_learning.algorithms.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](https://qiskit-community.github.io/qiskit-machine-learning/stubs/qiskit_machine_learning.connectors.TorchConnector.html#qiskit_machine_learning.connectors.TorchConnector),

which allows users to integrate all of our quantum neural networks directly into the

[PyTorch](https://pytorch.org)

open source machine learning library. Thanks to Qiskit’s gradient algorithms, this includes automatic

differentiation - the overall gradients computed by [PyTorch](https://pytorch.org)

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.



## Installation



We encourage installing Qiskit Machine Learning via the pip tool (a python package manager).



```bash

pip install qiskit-machine-learning

```



**pip** 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](https://qiskit-community.github.io/qiskit-machine-learning/getting_started.html#installation).



----------------------------------------------------------------------------------------------------



### Optional Installs



* **PyTorch**, may be installed either using command `pip install 'qiskit-machine-learning[torch]'` to install the

  package or refer to PyTorch [getting started](https://pytorch.org/get-started/locally/). When PyTorch

  is installed, the `TorchConnector` facilitates 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.



### Creating Your First Machine Learning Programming Experiment in Qiskit



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.



```python

from qiskit.circuit.library import TwoLocal, ZZFeatureMap

from qiskit_algorithms.optimizers import COBYLA

from qiskit_algorithms.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}")

```



### Further examples



Learning path notebooks may be found in the

[Machine Learning tutorials](https://qiskit-community.github.io/qiskit-machine-learning/tutorials/index.html) 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](https://github.com/Qiskit/textbook/tree/main/notebooks/quantum-machine-learning#) notebooks from the original Qiskit Textbook. The notebooks are 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 notebooks cover 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 notebooks are complementary to the tutorials of this module; whereas these tutorials focus

on actual Qiskit Machine Learning algorithms, the Textbook notebooks more explain and detail underlying fundamentals

of quantum machine learning.



----------------------------------------------------------------------------------------------------



## Contribution Guidelines



If you'd like to contribute to Qiskit, please take a look at our

[contribution guidelines](https://github.com/qiskit-community/qiskit-machine-learning/blob/main/CONTRIBUTING.md).

This project adheres to Qiskit's [code of conduct](https://github.com/qiskit-community/qiskit-machine-learning/blob/main/CODE_OF_CONDUCT.md).

By participating, you are expected to uphold this code.



We use [GitHub issues](https://github.com/qiskit-community/qiskit-machine-learning/issues) for tracking requests and bugs. Please

[join the Qiskit Slack community](https://qisk.it/join-slack)

and for discussion and simple questions.

For questions that are more suited for a forum, we use the **Qiskit** tag in [Stack Overflow](https://stackoverflow.com/questions/tagged/qiskit).



## Authors and Citation



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](https://github.com/qiskit-community/qiskit-machine-learning/graphs/contributors), who contribute

to the project at different levels.

If you use Qiskit, please cite as per the provided

[BibTeX file](https://github.com/Qiskit/qiskit/blob/main/CITATION.bib).



## License



This project uses the [Apache License 2.0](https://github.com/qiskit-community/qiskit-machine-learning/blob/main/LICENSE.txt).