Release v0.3.0

New features

• Can switch progress bar on and off (default is on) from the settings via settings.PROGRESSBAR = True/False.
  (#128)

• States in Gaussian and Fock representation now can be concatenated.

  from mrmustard.lab.states import Gaussian, Fock
  from mrmustard.lab.gates import Attenuator
  
  # concatenate pure states
  fock_state = Fock(4)
  gaussian_state = Gaussian(1)
  pure_state = fock_state & gaussian_state
  
  # also can concatenate mixed states
  mixed1 = fock_state >> Attenuator(0.8)
  mixed2 = gaussian_state >> Attenuator(0.5)
  mixed_state = mixed1 & mixed2
  
  mixed_state.dm()

  (#130)

• Parameter passthrough allows one to use custom variables and/or functions as parameters. For example we can use parameters of other gates:

  from mrmustard.lab.gates import Sgate, BSgate
  
  BS = BSgate(theta=np.pi/4, theta_trainable=True)[0,1]
  S0 = Sgate(r=BS.theta)[0]
  S1 = Sgate(r=-BS.theta)[1]
  
  circ = S0 >> S1 >> BS

  Another possibility is with functions:

  def my_r(x):
      return x**2
  
  x = math.new_variable(0.5, bounds = (None, None), name="x")
  
  def cost_fn():
    # note that my_r needs to be in the cost function
    # in order to track the gradient
    S = Sgate(r=my_r(x), theta_trainable=True)[0,1]
    return # some function of S
  
  opt.Optimize(cost_fn, by_optimizing=[x])

  (#131)

• Adds the new trainable gate RealInterferometer: an interferometer that doesn't mix the q and p quadratures. (#132)

• Now marginals can be iterated over:

  for mode in state:
    print(mode.purity)

  (#140)

Breaking changes

• The Parametrized and Training classes have been refactored: now trainable tensors are wrapped in an instance of the Parameter class. To define a set of parameters do

  from mrmustard.training import Parametrized
  
  params = Parametrized(
      magnitude=10, magnitude_trainable=False, magnitude_bounds=None,
      angle=0.1, angle_trainable=True, angle_bounds=(-0.1,0.1)
  )

  which will automatically define the properties magnitude and angle on the params object.
  To access the backend tensor defining the values of such parameters use the value property

  params.angle.value
  params.angle.bounds
  
  params.magnitude.value

  Gates will automatically be an instance of the Parametrized class, for example

  from mrmustard.lab import BSgate
  
  bs = BSgate(theta = 0.3, phi = 0.0, theta_trainable: True)
  
  # access params
  bs.theta.value
  bs.theta.bounds
  bs.phi.value

  (#133), patch (#144) and (#158).

Improvements

• The Parametrized and Training classes have been refactored. The new training module has been added and with it the new Parameter class: now trainable tensors are being wrapped in an instance of Parameter. (#133), patch (#144) and (#158).

• The string representations of the Circuit and Transformation objects have been improved: the Circuit.__repr__ method now produces a string that can be used to generate a circuit in an identical state (same gates and parameters), the Transformation.__str__ and objects inheriting from it now prints the name, memory location of the object as well as the modes of the circuit in which the transformation is acting on. The _markdown_repr_ has been implemented and on a jupyter notebook produces a table with valuable information of the Transformation objects. (#141)

• Add the argument modes to the Interferometer operation to indicate which modes the Interferometer is applied to. (#121)

Bug fixes

• Fixed a bug in the State.ket() method. An attribute was called with a typo in its name. (#135)

• The math.dagger function applying the hermitian conjugate to an operator was incorrectly transposing the indices of the input tensor. Now math.dagger appropriately calculates the Hermitian conjugate of an operator. (#156)

Documentation

• The centralized Xanadu Sphinx Theme is now used to style the Sphinx documentation. (#126)

• The documentation now contains the mm.training section. The optimization examples on the README and Basic API Reference section have been updated to use the latest API. (#133)

Contributors

This release contains contributions from (in alphabetical order):

Mikhail Andrenkov (@Mandrenkov), Sebastian Duque Mesa (@sduquemesa), Filippo Miatto (@ziofil), Yuan Yao (@sylviemonet)