Take a deeper dive into quantum photonic circuit algorithms by exploring cutting-edge algorithms using Strawberry Fields and near-term quantum hardware.

Learning Strawberry Fields

The tutorials below introduce the photonic quantum model used in Strawberry Fields. Make sure to read our circuit, operations, and states quickstarts in our documentation before diving in!

Optimization and machine learning

Strawberry Fields has a built-in TensorFlow backend, allowing backpropagation directly through quantum neural network simulations. Check out the following tutorials, showcasing how to train your photonic quantum program using classic machine learning techniques.


In addition to photonic circuit algorithms, Strawberry Fields provides high-level functions to perform algorithms on quantum hardware for a variety of applications, via the sf.apps module. For more details, see the Applications page.


The following tutorials showcase using Strawberry Fields to execute quantum programs on Xanadu’s quantum photonic hardware. Before you begin, be sure to check out the photonic hardware quickstart guide in the documentation. For more details on the photonic hardware, see the Hardware page.