Release v0.7.0

Release 0.7.0

New features

• Added a new interface for backends, as well as a numpy backend (which is now default). Users can run
  all the functions in the utils, math, physics, and lab with both backends, while training
  requires using tensorflow. The numpy backend provides significant improvements both in import
  time and runtime. (#301)

• Added the classes and methods to create, contract, and draw tensor networks with mrmustard.math.
  (#284)

• Added functions in physics.bargmann to join and contract (A,b,c) triples.
  (#295)

• Added an Ansatz abstract class and PolyExpAnsatz concrete implementation. This is used in the Bargmann representation.
  (#295)

• Added complex_gaussian_integral and real_gaussian_integral methods.
  (#295)

• Added Bargmann representation (parametrized by Abc). Supports all algebraic operations and CV (exact) inner product.
  (#296)

Breaking changes

• Removed circular dependencies by:

  • Removing graphics.py--moved ProgressBar to training and mikkel_plot to lab.
  • Moving circuit_drawer and wigner to physics.
  • Moving xptensor to math.
    (#289)

• Created settings.py file to host Settings.
  (#289)

• Moved settings.py, logger.py, and typing.py to utils.
  (#289)

• Removed the Math class. To use the mathematical backend, replace
  from mrmustard.math import Math ; math = Math() with import mrmustard.math as math
  in your scripts.
  (#301)

• The numpy backend is now default. To switch to the tensorflow
  backend, add the line math.change_backend("tensorflow") to your scripts.
  (#301)

Improvements

• Calculating Fock representations and their gradients is now more numerically stable (i.e. numerical blowups that
  result from repeatedly applying the recurrence relation are postponed to higher cutoff values).
  This holds for both the "vanilla strategy" (#274) and for the
  "diagonal strategy" and "single leftover mode strategy" (#288).
  This is done by representing Fock amplitudes with a higher precision than complex128 (countering floating-point errors).
  We run Julia code via PyJulia (where Numba was used before) to keep the code fast.
  The precision is controlled by setting settings.PRECISION_BITS_HERMITE_POLY. The default value is 128,
  which uses the old Numba code. When setting to a higher value, the new Julia code is run.

• Replaced parameters in training with Constant and Variable classes.
  (#298)

• Improved how states, transformations, and detectors deal with parameters by replacing the Parametrized class with ParameterSet.
  (#298)

• Includes julia dependencies into the python packaging for downstream installation reproducibility.
  Removes dependency on tomli to load pyproject.toml for version info, uses importlib.metadata instead.
  (#303)
  (#304)

• Improves the algorithms implemented in vanilla and vanilla_vjp to achieve a speedup.
  Specifically, the improved algorithms work on flattened arrays (which are reshaped before being returned) as opposed to multi-dimensional array.
  (#312)
  (#318)

• Adds functions hermite_renormalized_batch and hermite_renormalized_diagonal_batch to speed up calculating
  Hermite polynomials over a batch of B vectors.
  (#308)

• Added suite to filter undesired warnings, and used it to filter tensorflow's ComplexWarnings.
  (#332)

Bug fixes

• Added the missing shape input parameters to all methods U in the gates.py file.
  (#291)
• Fixed inconsistent use of atol in purity evaluation for Gaussian states.
  (#294)
• Fixed the documentations for loss_XYd and amp_XYd functions for Gaussian channels.
  (#305)
• Replaced all instances of np.empty with np.zeros to fix instabilities.
  (#309)

Documentation

Tests

• Added tests for calculating Fock amplitudes with a higher precision than complex128.

Contributors

@elib20 @rdprins @SamFerracin @jan-provaznik @sylviemonet @ziofil