Installation and downloads


Strawberry Fields requires the following libraries be installed:

as well as the following Python packages:

If you currently do not have Python 3 installed, we recommend Anaconda for Python 3, a distributed version of Python packaged for scientific computation.


Installation of Strawberry Fields, as well as all required Python packages mentioned above, can be installed via pip:

pip install strawberryfields

TensorFlow support

To use Strawberry Fields with TensorFlow, version 1.3 of TensorFlow is required. This can be installed alongside Strawberry Fields as follows:

pip install strawberryfields tensorflow==1.3

Or, to install Strawberry Fields and TensorFlow with GPU and CUDA support:

pip install strawberryfields tensorflow-gpu==1.3

Note that TensorFlow version 1.3 is only supported on Python versions less than 3.7. You can use the following command to check your Python version:

python --version

If the above prints out 3.7, and you still want to use TensorFlow, you will need to install Python 3.6. The recommended method is to install Anaconda for Python 3.

Once installed, you can then create a Python 3.6 Conda environment:

conda create --name sf_tensorflow_env python=3.6
conda activate sf_tensorflow_env
pip install strawberryfields tensorflow==1.3

Notebook downloads

Two of the tutorials provided in the documentation, quantum teleporation and Gaussian boson sampling, are also provided in the form of interactive Jupyter notebooks:

  1. QuantumTeleportation.ipynb
  2. GaussianBosonSampling.ipynb

To open them, launch the Jupyter notebook environment by clicking on the ‘Jupyter notebook’ shortcut in the start menu (Windows), or by running the following in the Anaconda Prompt/Command Prompt/Terminal:

jupyter notebook

Your web browser should open with the Jupyter notebook home page; simply click the ‘Upload’ button, browse to the tutorial file you downloaded above, and upload the file. You will now be able to open it and work through the tutorial.

Software tests

The Strawberry Fields test suite requires pytest and pytest-cov for coverage reports. These can both be installed via pip:

$ pip install pytest pytest-cov

To ensure that Strawberry Fields is working correctly after installation, the test suite can be run by navigating to the source code folder and running

$ make test

Note that this runs all of the tests, using all available backends, so can be quite slow (it should take around 40 minutes to complete). Alternatively, you can run the full test suite for a particular component by running

$ make test-[component]

where [component] should be replaced with either frontend for the Strawberry Fields frontend UI, or one of the backend you would like to test (fock, tf, or gaussian).

Pytest can accept a boolean logic string specifying exactly which tests to run, if finer control is needed. For example, to run all tests for the frontend and the Gaussian backend, as well as the Fock backend (but only for pure states), you can run:

$ make test-"gaussian or frontend or (fock and pure)"

The above syntax also works for the make coverage command, as well as make batch-test command for running the tests in batched mode.

Individual test modules are run by invoking pytest directly from the command line:

$ pytest tests/


Adding tests to Strawberry Fields

The tests folder is organised into three subfolders: backend for tests that only import a Strawberry Fields backend, frontend for tests that import the Strawberry Fields UI but do not make use of a backend, and integration for tests that test integration of the frontend and backends.

When writing new tests, make sure to mark what components it tests. For a backend test, you can use the backends mark, which accepts the names of the backends:

pytest.mark.backends("fock", "gaussian")

For a frontend-only test, you can use the frontend mark:



To build the documentation, the following additional packages are required:

If using Ubuntu, they can be installed via a combination of apt and pip:

$ sudo apt install graphviz
$ pip install sphinx --user
$ pip install sphinxcontrib-bibtex --user

To build the HTML documentation, go to the top-level directory and run

$ make docs

The documentation can then be found in the doc/_build/html/ directory.