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Release 0.14.0
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@josh146 josh146 released this 05 May 03:33
· 277 commits to master since this release
v0.14.0
7631271

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.

Assets 2