Bargmann method physics #295
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Codecov ReportAttention:
Additional details and impacted files@@ Coverage Diff @@
## develop #295 +/- ##
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+ Coverage 83.36% 83.97% +0.60%
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Files 61 64 +3
Lines 4448 4742 +294
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+ Hits 3708 3982 +274
- Misses 740 760 +20
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SamFerracin
reviewed
Oct 24, 2023
**Context:** Continue work to make Bargmann default **Description of the Change:** Pulls relevant code from MVP representation project (Data, MatVecData and AbcData classes) **Benefits:** We have the Bargmann representation now :)
SamFerracin
reviewed
Oct 30, 2023
SamFerracin
reviewed
Jan 8, 2024
sylviemonet
suggested changes
Jan 8, 2024
sylviemonet
approved these changes
Jan 11, 2024
Merged
SamFerracin
pushed a commit
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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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Context:
We want the Bargmann representation to take center stage
Description of the Change:
Benefits:
Can be used in the TN CV contractions and to support everything through Bargmann
Possible Drawbacks:
Need to be careful with the leftover index ordering