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fix the document about functions loss_XYd and amp_XYd #305

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merged 3 commits into from
Nov 17, 2023

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Context: The documentation of loss_XYd and amp_XYd is not correct, which are used to define two Gaussian channels: Attenuator and Amplifier.

Description of the Change: Change the wrong definition in the document part of both functions.

Benefits:

Possible Drawbacks:

Related GitHub Issues:

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codecov bot commented Nov 17, 2023

Codecov Report

Merging #305 (bbe203f) into develop (591e609) will decrease coverage by 0.03%.
Report is 2 commits behind head on develop.
The diff coverage is 100.00%.

Additional details and impacted files
@@             Coverage Diff             @@
##           develop     #305      +/-   ##
===========================================
- Coverage    82.35%   82.33%   -0.03%     
===========================================
  Files           60       60              
  Lines         4472     4466       -6     
===========================================
- Hits          3683     3677       -6     
  Misses         789      789              
Files Coverage Δ
mrmustard/_version.py 100.00% <100.00%> (ø)
mrmustard/physics/gaussian.py 88.00% <ø> (ø)

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@sylviemonet sylviemonet merged commit fba4c72 into develop Nov 17, 2023
7 checks passed
@sylviemonet sylviemonet deleted the channel_fix branch November 17, 2023 20:45
@SamFerracin SamFerracin mentioned this pull request Feb 1, 2024
SamFerracin pushed a commit that referenced this pull request Feb 6, 2024
### 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)](#301)

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

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

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

* Added `complex_gaussian_integral` and `real_gaussian_integral`
methods.
  [(#295)](#295)

* Added `Bargmann` representation (parametrized by Abc). Supports all
algebraic operations and CV (exact) inner product.
  [(#296)](#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)](#289)

* Created `settings.py` file to host `Settings`.
  [(#289)](#289)

* Moved `settings.py`, `logger.py`, and `typing.py` to `utils`.
  [(#289)](#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)](#301)

* The `numpy` backend is now default. To switch to the `tensorflow`
backend, add the line `math.change_backend("tensorflow")` to your
scripts.
  [(#301)](#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)](#274) and for the
"diagonal strategy" and "single leftover mode strategy"
[(#288)](#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)](#298)

* Improved how states, transformations, and detectors deal with
parameters by replacing the `Parametrized` class with `ParameterSet`.
  [(#298)](#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)](#303)
  [(#304)](#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)](#312)
  [(#318)](#318)

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

* Added suite to filter undesired warnings, and used it to filter
tensorflow's ``ComplexWarning``s.
  [(#332)](#332)


### Bug fixes

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

---------

Co-authored-by: Sebastián Duque Mesa <[email protected]>
Co-authored-by: JacobHast <[email protected]>
Co-authored-by: elib20 <[email protected]>
Co-authored-by: ziofil <[email protected]>
Co-authored-by: ziofil <[email protected]>
Co-authored-by: Luke Helt <[email protected]>
Co-authored-by: zeyueN <[email protected]>
Co-authored-by: Robbe De Prins <[email protected]>
Co-authored-by: Robbe De Prins (UGent-imec) <[email protected]>
Co-authored-by: Yuan <[email protected]>
Co-authored-by: Ryk <[email protected]>
Co-authored-by: Gabriele Gullì <[email protected]>
Co-authored-by: Yuan Yao <[email protected]>
Co-authored-by: Yuan Yao <[email protected]>
Co-authored-by: heltluke <[email protected]>
Co-authored-by: Tanner Rogalsky <[email protected]>
Co-authored-by: Jan Provazník <[email protected]>
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2 participants