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This repository was archived by the owner on Aug 21, 2023. It is now read-only.
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**Get the tutorials**<BR>
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For the full experience, you can start by downloading the latest release of the tutorials from [here](https://github.com/Qiskit/qiskit-tutorials/releases). Unzip the archive in the directory of your choice (this is the recommend way).
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For the full experience, you can start by downloading the latest release of the tutorials from [here](https://github.com/Qiskit/qiskit-tutorials/releases). Unzip the archive in the directory of your choice (this is the recommended way).
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To properly view and run the tutorials, you will need to install [Jupyter Notebook](https://jupyter.readthedocs.io/en/latest/install.html).
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## 2. Install Qiskit, Qiskit Aqua and Qiskit Aqua Chemistry
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At least [Python 3.5 or later](https://www.python.org/downloads/) is required to install and use Qiskit. If you have multiple Python versions installed (and particularly if the command `python --version` returns an incompatble version), you will need to ensure that your versions are [managed correctly](https://conda.io/docs/user-guide/tasks/manage-python.html). This can be done using the `environment.yml` file, as detailed below.
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When there are no issues with dependencies, Qiskit can be simply installed using
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When there are no issues with dependencies, Qiskit can be installed using
You can visualize your quantum circuits directly from Qiskit. Qiskit circuit drawers support Text, Latex and matplotlib. The text and matplotlib version is entirely native to Python, and thus easy to use. The Latex version produces publication-quality circuit images, but relies on some pre-requisite software. These include the `pdflatex` compiler for rendering latex documents, and the Poppler library for converting PDF to image. To get these:
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## 5. [Optional] Visualizing Circuits with LaTeX
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You can visualize your quantum circuits directly from Qiskit. Qiskit circuit drawers support text, LaTeX and matplotlib. The text and matplotlib version is entirely native to Python, and thus easy to use. The LaTeX version produces publication-quality circuit images, but relies on some pre-requisite software. These include the `pdflatex` compiler for rendering LaTeX documents, and the Poppler library for converting PDF to image. To get these:
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Welcome to the [Qiskit](https://www.qiskit.org/) tutorials!
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In this repository, we've put together a collection of Jupyter notebooks aimed at teaching people who want to use Qiskit for writing quantum computing programs, and executing them on one of several backends (online quantum processors, online simulators, and local simulators). The online quantum processors are the [IBM Q](https://quantumexperience.ng.bluemix.net/qx/devices) devices.
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In this repository, we've put together a collection of Jupyter notebooks aimed at teaching people who want to use Qiskit for writing quantum computing programs, and executing them on one of several backends (online quantum processors, online simulators, and local simulators). The online quantum processors are the [IBM Q](https://quantum-computing.ibm.com) devices.
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For our community-contributed tutorials, please check out the [qiskit-community-tutorials](https://github.com/Qiskit/qiskit-tutorials-community) repository.
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## Installation
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The notebooks for these tutorials can be viewed here on GitHub. But for the full experience, you'll want to interact with them!
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The notebooks for these tutorials can be viewed here on GitHub...but for the full experience, you'll want to interact with them!
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The easiest way to do this is using [the Binder image](https://mybinder.org/v2/gh/qiskit/qiskit-tutorials/master?filepath=./qiskit/start_here.ipynb), which lets you use the notebooks via the web. This means that you don't need to download or install anything, but it also means that you should not insert any private information into the notebooks (such as your API key). We recommend as pointed out in [issue #231](https://github.com/Qiskit/qiskit-tutorials/issues/231) that after you are done using mybinder that you regenerate your token.
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The easiest way to do this is using [the Binder image](https://mybinder.org/v2/gh/qiskit/qiskit-tutorials/master?filepath=./qiskit/start_here.ipynb), which lets you use the notebooks via the web. This means that you don't need to download or install anything, but it also means that you should not insert any private information into the notebooks (such as your API key). We recommend, as pointed out in [issue #231](https://github.com/Qiskit/qiskit-tutorials/issues/231), that after you are done using mybinder that you regenerate your token.
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Please refer to this [installation guide](INSTALL.md) for setting up Qiskit and the tutorials on your own machine (this is the recommended way).
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## Contents
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We have organized the tutorials into two sections:
We've collected a core reference set of notebooks in this section outlining the features of Qiskit. We will be keeping them up to date with the latest Qiskit version.
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-[Basics](qiskit/basics) is meant for those who are getting started.
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-[Terra](qiskit/terra) is meant for those who want to study circuits.
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-[Aqua](qiskit/aqua) is meant for those who want to develop applications on NISQ computers.
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-[Jupyter](qiskit/jupyter) is meant to highlight some cool Juypter features.
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To go through the official qiskit examples load up the [start_here.ipynb](qiskit/start_here.ipynb) notebook and start seeing how qiskit works.
Teaching quantum computing and qiskit has many different paths of learning. We love our community, and we love the contributions so keep them coming. Because Qiskit is changing so much, at the moment we cant keep this updated, but there are some great notebooks in here. See:
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-[Hello, Quantum World](community/hello_world/) learn from the community how to write your first quantum program.
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-[Quantum Games](community/games/), learn quantum computing by having fun.
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-[Quantum Information Science with Terra](community/terra/), learn about quantum information science with Qiskit Terra.
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-[Investigate Quantum Noise with Ignis](community/ignis/), learn about quantum noise with Qiskit Ignis.
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-[Textbook Quantum Algorithms](community/algorithms/), learn Qiskit from the textbook algorithms.
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-[Quantum Algorithms](community/aqua/), learn about quantum algorithms for noisy near-term devices with Qiskit Aqua.
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-[IBM Q Awards](community/awards/), learn from the great contributions to the [IBM Q Awards](https://qe-awards.mybluemix.net/), [Teach Me Qiskit 2018](community/awards/teach_me_qiskit_2018/index.ipynb) and [Teach Me Quantum 2018](community/awards/teach_me_quantum_2018/index.ipynb).
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To go through the community driven tutorials, load up the [index.ipynb](community/index.ipynb) notebook and start learning.
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To go through the Qiskit examples, load up the [start_here.ipynb](qiskit/start_here.ipynb) notebook and start seeing how Qiskit works.
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## Contribution Guidelines
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If you'd like to contribute to Qiskit Tutorials, please take a look at our
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[contribution guidelines](.github/CONTRIBUTING.md). This project adheres to Qiskit's [code of conduct](.github/CODE_OF_CONDUCT.md). By participating, you are expect to uphold to this code.
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We use [GitHub issues](https://github.com/Qiskit/qiskit-tutorials/issues) for tracking requests and bugs. Please use our [slack](https://qiskit.slack.com) for discussion and simple questions. To join our Slack community use the [link](https://join.slack.com/t/qiskit/shared_invite/enQtNDc2NjUzMjE4Mzc0LTMwZmE0YTM4ZThiNGJmODkzN2Y2NTNlMDIwYWNjYzA2ZmM1YTRlZGQ3OGM0NjcwMjZkZGE0MTA4MGQ1ZTVmYzk). For questions that are more suited for a forum we use the Qiskit tag in the [Stack Exchange](https://quantumcomputing.stackexchange.com/questions/tagged/qiskit).
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We use [GitHub issues](https://github.com/Qiskit/qiskit-tutorials/issues) for tracking requests and bugs. Please use our [slack](https://qiskit.slack.com) for discussion and simple questions. To join our Slack community, use the [link](https://join.slack.com/t/qiskit/shared_invite/enQtNDc2NjUzMjE4Mzc0LTMwZmE0YTM4ZThiNGJmODkzN2Y2NTNlMDIwYWNjYzA2ZmM1YTRlZGQ3OGM0NjcwMjZkZGE0MTA4MGQ1ZTVmYzk). For questions that are more suited for a forum, we use the Qiskit tag in the [Stack Exchange](https://quantumcomputing.stackexchange.com/questions/tagged/qiskit).
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