শুরুর নির্দেশনা¶

স্থাপন (ইনস্টলেশন)¶

Let's get started using Qiskit! The first thing to do is choose how you're going to run and install the packages. There are three main ways to do this:

Start locally

Qiskit supports Python 3.7 or later. However, both Python and Qiskit are evolving ecosystems, and sometimes when new releases occur in one or the other, there can be problems with compatibility.

We recommend installing Anaconda, a cross-platform Python distribution for scientific computing. Jupyter, included in Anaconda, is recommended for interacting with Qiskit.

We recommend using Python virtual environments to cleanly separate Qiskit from other applications and improve your experience.

The simplest way to use environments is by using the conda command, included with Anaconda. A Conda environment allows you to specify a specific version of Python and set of libraries. Open a terminal window in the directory where you want to work.

It is preferred that you use the Anaconda prompt installed with Anaconda. All you have to do is create a virtual environment inside Anaconda and activate the environment. These commands can be run in the Anaconda prompt irrespective of Windows or Linux machine.

Create a minimal environment with only Python installed in it.

conda create -n ENV_NAME python=3

Activate your new environment.

conda activate ENV_NAME

Next, install the Qiskit package.

pip install qiskit

If the packages were installed correctly, you can run conda list to see the active packages in your virtual environment.

If you intend to use visualization functionality or Jupyter notebooks it is recommended to install Qiskit with the extra visualization support:

pip install qiskit[visualization]

It is worth pointing out that if you're a zsh user (which is the default shell on newer versions of macOS), you'll need to put qiskit[visualization] in quotes:

pip install 'qiskit[visualization]'

Start on the cloud

The following cloud vendors have Qiskit pre-installed in their environments:

IBM Quantum Lab

Build quantum applications and experiments with Qiskit in a cloud programming environment.

Strangeworks

A platform that enables users and organizations to easily apply quantum computing to their most pressing problems and research.

Install from source

Installing the elements from source allows you to access the most recently updated version of Qiskit instead of using the version in the Python Package Index (PyPI) repository. This will give you the ability to inspect and extend the latest version of the Qiskit code more efficiently.

When installing the elements and components from source, by default their development version (which corresponds to the master git branch) will be used, as opposed to the stable version (which contains the same codebase as the published pip packages). Since the development versions of an element or component usually include new features and changes, they generally require using the development version of the rest of the items as well.

The Terra and Aer packages both require a compiler to build from source before you can install. Ignis, Aqua, and the IBM Quantum Provider backend do not require a compiler.

Installing elements from source requires the following order of installation to prevent installing versions of elements that may be lower than those desired if the pip version is behind the source versions:

qiskit-terra
qiskit-aer
qiskit-ibmq-provider (if you want to connect to the IBM Quantum devices or online simulator)

To work with several components and elements simultaneously, use the following steps for each element.

নোট

Due to the use of namespace packaging in Python, care must be taken in how you install packages. If you're planning to install any element from source, do not use the qiskit meta-package. Also, follow this guide and use a separate virtual environment for development. If you do choose to mix an existing installation with your development, refer to https://github.com/pypa/sample-namespace-packages/blob/master/table.md for the set of combinations of installation methods that work together.

Set up the Virtual Development Environment

conda create -y -n QiskitDevenv python=3
conda activate QiskitDevenv

Installing Terra from Source

Installing from source requires that you have a C++ compiler on your system that supports C++11.

Compiler for Linux

On most Linux platforms, the necessary GCC compiler is already installed.

Compiler for macOS

If you use macOS, you can install the Clang compiler by installing XCode. Check if you have XCode and Clang installed by opening a terminal window and entering the following.

clang --version

Install XCode and Clang by using the following command.

xcode-select --install

Compiler for Windows

On Windows, it is easiest to install the Visual C++ compiler from the Build Tools for Visual Studio 2019. You can instead install Visual Studio version 2015 or 2017, making sure to select the options for installing the C++ compiler.

Once the compilers are installed, you are ready to install Qiskit Terra.

Clone the Terra repository.

git clone https://github.com/Qiskit/qiskit-terra.git

Cloning the repository creates a local folder called qiskit-terra.
```
cd qiskit-terra
```
Install the Python requirements libraries from your qiskit-terra directory.
```
pip install cython
```
If you want to run tests or linting checks, install the developer requirements.
```
pip install -r requirements-dev.txt
```
Install qiskit-terra.
```
pip install .
```

If you want to install it in editable mode, meaning that code changes to the project don't require a reinstall to be applied, you can do this with:

pip install -e .

You can then run the code examples after installing Terra. You can run the example with the following command.

python examples/python/using_qiskit_terra_level_0.py

নোট

If you do not intend to install any other components, qiskit-terra will emit a RuntimeWarning warning that both qiskit-aer and qiskit-ibmq-provider are not installed. This is done because users commonly intend to use the additional elements, but do not realize they are not installed, or that the installation of either Aer or the IBM Quantum Provider failed for some reason. If you wish to suppress these warnings, add:

import warnings
warnings.filterwarnings('ignore', category=RuntimeWarning,
                        module='qiskit')

before any qiskit imports in your code. This will suppress the warning about the missing qiskit-aer and qiskit-ibmq-provider, but will continue to display any other warnings from qiskit or other packages.

Installing Aer from Source

Clone the Aer repository.

git clone https://github.com/Qiskit/qiskit-aer

Install build requirements.
```
pip install cmake scikit-build cython
```

After this, the steps to install Aer depend on which operating system you are using. Since Aer is a compiled C++ program with a Python interface, there are non-Python dependencies for building the Aer binary which can't be installed universally depending on operating system.

Qiskit Aer is a high performance simulator framework for quantum circuits. It provides several backends to achieve different simulation goals.

conda install --update-deps -c conda-forge -y openblas cmake

Build and install qiskit-aer directly

If you have pip <19.0.0 installed and your environment doesn't require a custom build, run:
```
cd qiskit-aer
pip install .
```
This will both build the binaries and install Aer.

Alternatively, if you have a newer pip installed, or have some custom requirement, you can build a Python wheel manually.
```
cd qiskit-aer
python ./setup.py bdist_wheel
```
If you need to set a custom option during the wheel build, refer to aer_wheel_build_options.

After you build the Python wheel, it will be stored in the dist/ dir in the Aer repository. The exact version will depend
```
cd dist
pip install qiskit_aer-*.whl
```
The exact filename of the output wheel file depends on the current version of Aer under development.

Custom options

The Aer build system uses scikit-build to run the compilation when building it with the Python interface. It acts as an interface for setuptools to call CMake and compile the binaries for your local system.

Due to the complexity of compiling the binaries, you may need to pass options to a certain part of the build process. The way to pass variables is:

python setup.py bdist_wheel [skbuild_opts] [-- [cmake_opts] [-- build_tool_opts]]

where the elements within square brackets [] are optional, and skbuild_opts, cmake_opts, build_tool_opts are to be replaced by flags of your choice. A list of CMake options is available here: https://cmake.org/cmake/help/v3.6/manual/cmake.1.html#options. For example, you could run something like:

python setup.py bdist_wheel -- -- -j8

This is passing the flag -j8 to the underlying build system (which in this case is Automake), telling it that you want to build in parallel using 8 processes.

For example, a common use case for these flags on linux is to specify a specific version of the C++ compiler to use (normally if the default is too old):

python setup.py bdist_wheel -- -DCMAKE_CXX_COMPILER=g++-7

which will tell CMake to use the g++-7 command instead of the default g++ when compiling Aer.

Another common use case for this, depending on your environment, is that you may need to specify your platform name and turn off static linking.

python setup.py bdist_wheel --plat-name macosx-10.9-x86_64 \
-- -DSTATIC_LINKING=False -- -j8

Here --plat-name is a flag to setuptools, to specify the platform name to use in the package metadata, -DSTATIC_LINKING is a flag for using CMake to disable static linking, and -j8 is a flag for using Automake to use 8 processes for compilation.

A list of common options depending on platform are:

Platform	Tool	Option	Use Case
All	Automake	-j	Followed by a number, sets the number of processes to use for compilation.
Linux	CMake	-DCMAKE_CXX_COMPILER	Used to specify a specific C++ compiler; this is often needed if your default g++ is too old.
OSX	setuptools	--plat-name	Used to specify the platform name in the output Python package.
OSX	CMake	-DSTATIC_LINKING	Used to specify whether or not static linking should be used.

নোট

Some of these options are not platform-specific. These particular platforms are listed because they are commonly used in the environment. Refer to the tool documentation for more information.

Installing IBM Quantum Provider from Source

Clone the qiskit-ibmq-provider repository.

git clone https://github.com/Qiskit/qiskit-ibmq-provider.git

Cloning the repository creates a local directory called qiskit-ibmq-provider.
```
cd qiskit-ibmq-provider
```
If you want to run tests or linting checks, install the developer requirements. This is not required to install or use the qiskit-ibmq-provider package when installing from source.
```
pip install -r requirements-dev.txt
```
Install qiskit-ibmq-provider.
```
pip install .
```

If you want to install it in editable mode, meaning that code changes to the project don't require a reinstall to be applied:

pip install -e .

Platform Support¶

Qiskit strives to support as many platforms as possible, but due to limitations in available testing resources and platform availability, not all platforms can be supported. Platform support for Qiskit is broken into 3 tiers with different levels of support for each tier. For platforms outside these, Qiskit is probably still installable, but it's not tested and you will have to build Qiskit (and likely Qiskit's dependencies) from source.

Additionally, Qiskit only supports CPython. Running with other Python interpreters isn't currently supported.

Tier 1¶

Tier 1 supported platforms are fully tested upstream as part of the development processes to ensure any proposed change will function correctly. Pre-compiled binaries are built, tested, and published to PyPI as part of the release process. These platforms are expected to be installable with just a functioning Python environment as all dependencies are available on these platforms.

Tier 1 platforms are currently:

Linux x86_64 (distributions compatible with the manylinux 2014 packaging specification.

macOS x86_64 (10.9 or newer)

Windows 64 bit

Tier 2¶

Tier 2 platforms are not tested upstream as part of development process. However, pre-compiled binaries are built, tested, and published to PyPI as part of the release process and these packages can be expected to be installed with just a functioning Python environment.

Tier 2 platforms are currently:

Linux i686 (distributions compatible with the manylinux 2014 packaging specification) for Python < 3.10

Windows 32 bit for Python < 3.10

Linux aarch64 (distributions compatible with the manylinux 2014 packaging specification)

Tier 3¶

Tier 3 platforms are not tested upstream as part of the development process. Pre-compiled binaries are built and published to PyPI as part of the release process, with no testing at all. They may not be installable with just a functioning Python environment and may require a C/C++ compiler or additional programs to build dependencies from source as part of the installation process. Support for these platforms are best effort only.

Tier 3 platforms are currently:

Linux ppc64le (distributions compatible with the manylinux 2014 packaging specification)

Linux s390x (distributions compatible with the manylinux 2014 packaging specification)

macOS arm64 (10.15 or newer)

Linux i686 (distributions compatible with the manylinux 2014 packaging specification) for Python >= 3.10

Windows 32 bit for Python >= 3.10

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