Strawberry Fields v0.14.0 Released

We’re very excited to announce the release of Strawberry Fields version 0.14.0. :strawberry:

This release is focused on updating the "tf" backend to support TensorFlow 2.0 and above. You can now use the features from TensorFlow 2 for building deep learning models in Strawberry Fields.


TensorFlow 2.0 :robot:

Eager execution is now implemented by default in TensorFlow and thus also the default mode in the "tf" backend in Strawberry Fields — there is no longer any need to create a tf.Session(). You can now use tf.GradientTape() for gradient calculations.

See https://www.tensorflow.org/guide/migrate for help with migrating your TensorFlow 1 code to TensorFlow 2.

Below is an example for using TensorFlow 2.0 to train a variational photonic circuit:

Code example
eng = sf.Engine(backend="tf", backend_options={"cutoff_dim": 7})
prog = sf.Program(1)

with prog.context as q:
   # Apply a single mode displacement with free parameters
   Dgate(prog.params("a"), prog.params("p")) | q[0]

   opt = tf.keras.optimizers.Adam(learning_rate=0.1)

   alpha = tf.Variable(0.1)
   phi = tf.Variable(0.1)

   for step in range(50):

       # reset the engine if it has already been executed
       if eng.run_progs:
           eng.reset()

       with tf.GradientTape() as tape:
           # execute the engine
           results = eng.run(prog, args={'a': alpha, 'p': phi})
           # get the probability of fock state |1>
           prob = results.state.fock_prob([1])
           # negative sign to maximize prob
           loss = -prob

       gradients = tape.gradient(loss, [alpha, phi])
       opt.apply_gradients(zip(gradients, [alpha, phi]))
       print("Value at step {}: {}".format(step, prob))

For more details and demonstrations of the new TensorFlow 2.0-compatible backend, see our optimization and machine learning tutorials.


The full release notes are available at https://github.com/XanaduAI/strawberryfields/releases/tag/v0.14.0.

As always, this release would not have been possible without all the help from our contributors:

@Tom_Bromley, @theodor, @josh, @nathan, Filippo Miatto, @Nicolas_Quesada, @antalszava, Paul Tan

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