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Installation instructions

There are two primary ways to do run and install the packages:

Pre-installation

To install locally (using either :ref:`Option 1` or :ref:`Option 2`), follow a brief set of common instructions to prepare a Python environment for installation of AQC-Tensor:

First, create a minimal environment with only Python installed in it. We recommend using Python virtual environments.

python3 -m venv /path/to/virtual/environment

Activate your new environment.

source /path/to/virtual/environment/bin/activate

Note: If you are using Windows, use the following commands in PowerShell:

python3 -m venv c:\path\to\virtual\environment
c:\path\to\virtual\environment\Scripts\Activate.ps1

Option 1: Pip installation

Upgrade pip and install the AQC-Tensor package. To meaningfully use the package, you must also install at least one tensor network backend. The following snippet installs the addon, along with quimb (for tensor network support) and jax (for automatic differentiation).

pip install --upgrade pip
pip install 'qiskit-addon-aqc-tensor[quimb-jax]'

Option 2: Install from source

To develop in the repository or to run the tutorials locally, install from source:

In either case, the first step is to clone the AQC-Tensor repository.

git clone git@github.com:Qiskit/qiskit-addon-aqc-tensor.git

Next, upgrade pip and enter the repository.

pip install --upgrade pip
cd qiskit-addon-aqc-tensor

The next step is to install AQC-Tensor to the virtual environment. If you plan on running the tutorials, install the notebook dependencies in order to run all the visualizations in the notebooks. If you plan on developing in the repository, install the dev dependencies.

Adjust the options below to suit your needs.

pip install tox jupyterlab -e '.[notebook-dependencies,dev]'

If you installed the notebook dependencies, you can get started with AQC-Tensor by running the notebooks in the docs.

cd docs/
jupyter lab

Platform support

We expect this package to work on any Tier 1 platform supported by Qiskit.