Adding function to draw tensor networks #284
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**Context:** This PR manages to - bump tensorflow to its latest version v2.14, on all platforms (no longer v2.12 as the branch name is suggesting) - add native M1 Mac support! (outside of devcontainers) - improve poetry installation without the need to reinstall tensorflow with pip **Description of the Change:** - pyproject.toml updated with better TF dependency handling (documented in comments) - updated lock file - TF API change: Adam -> legacy.Adam - TF API change: concat of scalars uses stack now **Benefits:** NATIVE, MAC, SUPPORT **Possible Drawbacks:** pyproject.toml got more complex? **Related GitHub Issues:** --------- Co-authored-by: heltluke <luke.helt@gmail.com> Co-authored-by: Luke Helt <31250931+heltluke@users.noreply.github.com>
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### 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 <675763+sduquemesa@users.noreply.github.com> Co-authored-by: JacobHast <jacobhastrup@gmail.com> Co-authored-by: elib20 <53090166+elib20@users.noreply.github.com> Co-authored-by: ziofil <ziofil@users.noreply.github.com> Co-authored-by: ziofil <miatto@gmail.com> Co-authored-by: Luke Helt <31250931+heltluke@users.noreply.github.com> Co-authored-by: zeyueN <48225584+zeyueN@users.noreply.github.com> Co-authored-by: Robbe De Prins <52749580+rdprins@users.noreply.github.com> Co-authored-by: Robbe De Prins (UGent-imec) <Robbe.DePrins@UGent.be> Co-authored-by: Yuan <16817699+sylviemonet@users.noreply.github.com> Co-authored-by: Ryk <47638463+ryk-wolf@users.noreply.github.com> Co-authored-by: Gabriele Gullì <120967042+ggulli@users.noreply.github.com> Co-authored-by: Yuan Yao <yuan.yao@xanadu-Yuan-YAO.local> Co-authored-by: Yuan Yao <yuan.yao@xanadu-infras-MacBook-Air.local> Co-authored-by: heltluke <luke.helt@gmail.com> Co-authored-by: Tanner Rogalsky <tanner@tannerrogalsky.com> Co-authored-by: Jan Provazník <101715194+jan-provaznik@users.noreply.github.com>
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Context:
Adding function to draw tensor networks. The function relies on
networkxto find the best way of laying the nodes out.Attached is an example, obtained by connecting the modes on an Sgate, a Dgate, and a BSgate