StrawberryFields 0.14.0

Release 0.14.0

on Python PyPI

New features since last release

• The "tf" backend now supports TensorFlow 2.0 and above. (#283) (#320) (#323) (#361) (#372) (#373) (#374) (#375) (#377)

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

  For example, using TensorFlow 2.0 to train a variational photonic circuit:

  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))

• Adds the method number_expectation that calculates the expectation value of the product of the number operators of a given set of modes. (#348)

  prog = sf.Program(3)
  with prog.context as q:
      ops.Sgate(0.5) | q[0]
      ops.Sgate(0.5) | q[1]
      ops.Sgate(0.5) | q[2]
      ops.BSgate(np.pi/3, 0.1) |  (q[0], q[1])
      ops.BSgate(np.pi/3, 0.1) |  (q[1], q[2])

  Executing this on the Fock backend,

  >>> eng = sf.Engine("fock", backend_options={"cutoff_dim": 10})
  >>> state = eng.run(prog).state

  we can compute the expectation value <n_0 n_2>:

  >>> state.number_expectation([0, 2])

Improvements

• Add details to the error message for failed remote jobs. (#370)

• The required version of The Walrus was increased to version 0.12, for
  tensor number expectation support. (#380)

Contributors

This release contains contributions from (in alphabetical order):

Tom Bromley, Theodor Isacsson, Josh Izaac, Nathan Killoran, Filippo Miatto, Nicolás Quesada, Antal Száva, Paul Tan.