StrawberryFields 0.15.0

Release 0.15.0

on Python PyPI

New features since last release

• Adds the ability to train variational GBS circuits in the applications layer. (#387) (#388) (#391) (#393) (#414) (#415)

  Trainable parameters can be embedded into a VGBS class:

  from strawberryfields.apps import data, train
  
  d = data.Mutag0()
  embedding = train.Exp(d.modes)
  n_mean = 5
  
  vgbs = train.VGBS(d.adj, 5, embedding, threshold=False, samples=np.array(d[:1000]))

  Properties of the variational GBS distribution for different choices of trainable parameters can then be inspected:

  >>> params = 0.1 * np.ones(d.modes)
  >>> vgbs.n_mean(params)
  3.6776094165797364

  A cost function can then be created and its value and gradient accessed:

  >>> h = lambda x: np.sum(x)
  >>> cost = train.Stochastic(h, vgbs)
  >>> cost(params, n_samples=1000)
  3.940396998165503
  >>> cost.grad(params, n_samples=1000)
  array([-0.54988876, -0.49270263, -0.6628071 , -1.13057762, -1.13568456,
       -0.70180571, -0.6266806 , -0.68803539, -1.11032533, -1.12853718,
       -0.59172261, -0.47830748, -0.96901676, -0.66938217, -0.85162006,
       -0.27188134, -0.26955011])

  For more details, see the VGBS training demo.

• Feature vectors of graphs can now be calculated exactly in the apps.similarity module of the applications layer. Datasets of pre-calculated feature vectors are available in apps.data. (#390) (#401)

  >>> from strawberryfields.apps import data
  >>> from strawberryfields.apps.similarity import feature_vector_sampling
  >>> samples = data.Mutag0()
  >>> feature_vector_sampling(samples, [2, 4, 6])
  [0.19035, 0.2047, 0.1539]

  For more details, see the graph similarity demo.

• A new strawberryfields.apps.qchem module has been introduced, centralizing all quantum chemistry applications. This includes various new features and improvements:

  • Adds the apps.qchem.duschinsky() function for generation of the Duschinsky rotation matrix and displacement vector which are needed to simulate a vibronic process with Strawberry Fields. (#434)

  • Adds the apps.qchem.dynamics module for simulating vibrational quantum dynamics in molecules. (#402) (#411) (#419) (#421) (#423) (#430)

    This includes:

    • dynamics.evolution() constructs a custom operation that encodes the input chemical information. This custom operation can then be used within a Strawberry Fields Program.

    • dynamics.sample_coherent(), dynamics.sample_fock() and dynamics.sample_tmsv() functions allow for generation of samples from a variety of input states.

    • The probability of an excited state can then be estimated with the dynamics.prob() function, which calculates the relative frequency of the excited state among the generated samples.

    • Finally, the dynamics.marginals() function generates marginal distributions.

  • The sf.apps.vibronic module has been relocated to within the qchem module. As a result, the apps.sample.vibronic() function is now accessible under apps.qchem.vibronic.sample(), providing a single location for quantum chemistry functionality. (#416)

  For more details, please see the qchem documentation.

• The GaussianState returned from simulations using the Gaussian backend now has feature parity with the FockState object returned from the Fock backends. (#407)

  In particular, it now supports the following methods:

  • GaussianState.dm()
  • GaussianState.ket()
  • GaussianState.all_fock_probs()

  In addition, the existing GaussianState.reduced_dm() method now supports multi-mode reduced density matrices.

• Adds the sf.utils.samples_expectation, sf.utils.samples_variance and sf.utils.all_fock_probs_pnr functions for obtaining counting statistics from samples. (#399)

• Compilation of Strawberry Fields programs has been overhauled.

  • Strawberry Fields can now access the Xanadu Cloud device specifications API. The Connection class has a new method Connection.get_device, which returns a DeviceSpec class. (#429) (#432)

  • New Xstrict, Xcov, and Xunitary compilers for compiling programs into the X architecture have been added. (#358) (#438)

  • Finally, the strawberryfields.circuitspecs module has been renamed to strawberryfields.compilers.

• Adds diagonal_expectation method for the BaseFockState class, which returns the expectation value of any operator that is diagonal in the number basis. (#389)

• Adds parity_expectation method as an instance of diagonal_expectation for the BaseFockState class, and its own function for BaseGaussianState. This returns the expectation value of the parity operator, defined as (-1)^N. (#389)

Improvements

• Modifies the rectangular interferometer decomposition to make it more efficient for hardware devices. Rather than decomposing the interferometer using Clements :math:T matrices, the decomposition now directly produces Mach-Zehnder interferometers corresponding to on-chip phases. (#363)

• Changes the number_expectation method for the BaseFockState class to be an instance of diagonal_expectation. (#389)

• Increases the speed at which the following gates are generated: Dgate, Sgate, BSgate and S2gate by relying on a recursive implementation recently introduced in thewalrus. This has substantial effects on the speed of the Fockbackend and the TFbackend, especially for high cutoff values. (#378) (#381)

• All measurement samples can now be accessed via the results.all_samples attribute, which returns a dictionary mapping the mod index to a list of measurement values. This is useful for cases where a single mode may be measured multiple times. (#433)

Breaking Changes

• Removes support for Python 3.5. (#385)

• Complex parameters now are expected in polar form as two separate real parameters. (#378)

Contributors

This release contains contributions from (in alphabetical order):

Juan Miguel Arrazola, Tom Bromley, Jack Ceroni, Aroosa Ijaz, Theodor Isacsson, Josh Izaac, Nathan Killoran, Soran Jahangiri, Shreya P. Kumar, Filippo Miatto, Nicolás Quesada, Antal Száva