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https://github.com/microsoft/MLADS2020-QuantumClassification

Quantum Classification tutorial using Microsoft Quantum Development Kit
https://github.com/microsoft/MLADS2020-QuantumClassification

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Quantum Classification tutorial using Microsoft Quantum Development Kit

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README 

        
***This repository has been archived. Please refer to [the tutorial on quantum classification with Q#](https://github.com/microsoft/QuantumKatas/tree/main/tutorials/QuantumClassification) for the latest version of the tutorial.***



# Welcome!



This repository contains the materials for the "Introduction to Quantum Classification" lab presented at Spring 2020 MLADS Conference. In this lab you will experiment with a simple circuit-centric quantum classifier using the Microsoft Quantum Development Kit.



## Running the Lab



You can run the lab online [on Binder](https://mybinder.org/v2/gh/Microsoft/MLADS2020-QuantumClassification/master?filepath=QuantumClassification/ExploringQuantumClassificationLibrary.ipynb). 

> Running this lab on Binder is *very* slow, so if you prefer to run the lab online we recommend you to read through the notebook using saved cell outputs before attempting to run the cells.



Alternatively, you can [install Jupyter and Q#](https://docs.microsoft.com/quantum/install-guide/qjupyter) and 

[qsharp package for Python](https://docs.microsoft.com/en-us/quantum/install-guide/pyinstall). Note that this tutorial requires `matplotlib` and `numpy` Python packages to be installed.



After that you can run the tutorial locally by navigating to the `QuantumClassification` folder and starting the notebook from command line using the following command:



    jupyter notebook ExploringQuantumClassificationLibrary.ipynb



The Q# project in this folder contains the back-end of the tutorial and is not designed for direct use.



## Resources



Here are some resources to get you started:



* [Install Microsoft Quantum Development Kit and Q# Jupyter Notebooks](https://docs.microsoft.com/en-us/quantum/install-guide/qjupyter)

* Learn the basics of quantum computing and Q# using the [Quantum Katas](https://github.com/Microsoft/QuantumKatas/)

* Check out [Microsoft Quantum documentation](https://docs.microsoft.com/quantum), in particular [introduction to quantum machine learning](https://docs.microsoft.com/quantum/libraries/machine-learning/)

* ['Circuit-centric quantum classifiers', by Maria Schuld, Alex Bocharov, Krysta Svore and Nathan Wiebe](https://arxiv.org/abs/1804.00633) describes the original proposal behind this type of classifiers.



## Contributing



This project welcomes contributions and suggestions.  Most contributions require you to agree to a

Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us

the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.



When you submit a pull request, a CLA bot will automatically determine whether you need to provide

a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions

provided by the bot. You will only need to do this once across all repos using our CLA.



This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).

For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or

contact [[email protected]](mailto:[email protected]) with any additional questions or comments.