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# Release 0.5.0 ### New features * Optimization callback functionalities has been improved. A dedicated `Callback` class is added which is able to access the optimizer, the cost function, the parameters as well as gradients, during the optimization. In addition, multiple callbacks can be specified. This opens up the endless possiblities of customizing the the optimization progress with schedulers, trackers, heuristics, tricks, etc. [(#219)](#219) * Tensorboard-based optimization tracking is added as a builtin `Callback` class: `TensorboardCallback`. It can automatically track costs as well as all trainable parameters during optimization in realtime. Tensorboard can be most conveniently viewed from VScode. [(#219)](#219) ```python import numpy as np from mrmustard.training import Optimizer, TensorboardCallback def cost_fn(): ... def as_dB(cost): delta = np.sqrt(np.log(1 / (abs(cost) ** 2)) / (2 * np.pi)) cost_dB = -10 * np.log10(delta**2) return cost_dB tb_cb = TensorboardCallback(cost_converter=as_dB, track_grads=True) opt = Optimizer(euclidean_lr = 0.001); opt.minimize(cost_fn, max_steps=200, by_optimizing=[...], callbacks=tb_cb) # Logs will be stored in `tb_cb.logdir` which defaults to `./tb_logdir/...` but can be customized. # VScode can be used to open the Tensorboard frontend for live monitoring. # Or, in command line: `tensorboard --logdir={tb_cb.logdir}` and open link in browser. ``` * Gaussian states support a `bargmann` method for returning the bargmann representation. [(#235)](#235) * The `ket` method of `State` now supports new keyword arguments `max_prob` and `max_photons`. Use them to speed-up the filling of a ket array up to a certain probability or *total* photon number. [(#235)](#235) ```python from mrmustard.lab import Gaussian # Fills the ket array up to 99% probability or up to the |0,3>, |1,2>, |2,1>, |3,0> subspace, whichever is reached first. # The array has the autocutoff shape, unless the cutoffs are specified explicitly. ket = Gaussian(2).ket(max_prob=0.99, max_photons=3) ``` * Gaussian transformations support a `bargmann` method for returning the bargmann representation. [(#239)](#239) * BSGate.U now supports method='vanilla' (default) and 'schwinger' (slower, but stable to any cutoff) [(#248)](#248) ### Breaking Changes * The previous `callback` argument to `Optimizer.minimize` is now `callbacks` since we can now pass multiple callbacks to it. [(#219)](#219) * The `opt_history` attribute of `Optimizer` does not have the placeholder at the beginning anymore. [(#235)](#235) ### Improvements * The math module now has a submodule `lattice` for constructing recurrence relation strategies in the Fock lattice. There are a few predefined strategies in `mrmustard.math.lattice.strategies`. [(#235)](#235) * Gradients in the Fock lattice are now computed using the vector-jacobian product. This saves a lot of memory and speeds up the optimization process by roughly 4x. [(#235)](#235) * Tests of the compact_fock module now use hypothesis. [(#235)](#235) * Faster implementation of the fock representation of `BSgate`, `Sgate` and `SqueezedVacuum`, ranging from 5x to 50x. [(#239)](#239) * More robust implementation of cutoffs for States. [(#239)](#239) * Dependencies and versioning are now managed using Poetry. [(#257)](#257) ### Bug fixes * Fixed a bug that would make two progress bars appear during an optimization [(#235)](#235) * The displacement of the dual of an operation had the wrong sign [(#239)](#239) * When projecting a Gaussian state onto a Fock state, the upper limit of the autocutoff now respect the Fock projection. [(#246)](#246) * Fixed a bug for the algorithms that allow faster PNR sampling from Gaussian circuits using density matrices. When the cutoff of the first detector is equal to 1, the resulting density matrix is now correct. ### Documentation ### Contributors [Filippo Miatto](https://github.com/ziofil), [Zeyue Niu](https://github.com/zeyueN), [Robbe De Prins](https://github.com/rdprins), [Gabriele Gullì](https://github.com/ggulli), [Richard A. Wolf](https://github.com/ryk-wolf) --------- Co-authored-by: Sebastián Duque Mesa <[email protected]> Co-authored-by: JacobHast <[email protected]> Co-authored-by: elib20 <[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]>
# Release 0.6.0 (current release) ### New features * Added a new method to discretize Wigner functions that revolves Clenshaw summations. This method is expected to be fast and reliable for systems with high number of excitations, for which the pre-existing iterative method is known to be unstable. Users can select their preferred methods by setting the value of `Settings.DISCRETIZATION_METHOD` to either `interactive` (default) or `clenshaw`. * Added the `PhaseNoise(phase_stdev)` gate (non-Gaussian). Output is a mixed state in Fock representation. It is not based on a choi operator, but on a nonlinear transformation of the density matrix. [(#275)](#275) ### Breaking changes * The value of `hbar` can no longer be specified outside of `Settings`. All the classes and methods that allowed specifying its value as an input now retrieve it directly from `Settings`. * Certain attributes of `Settings` can no longer be changed after their value is queried for the first time. ### Improvements * Tensorflow bumped to v2.14 with poetry installation working out of the box on Linux and Mac. [(#281)](#281) ### Bug fixes * Fixed a bug about the variable names in functions (apply_kraus_to_ket, apply_kraus_to_dm, apply_choi_to_ket, apply_choi_to_dm). [(#271)](#271) * Fixed a bug that was leading to an error when computing the Choi representation of a unitary transformation. ### Documentation ### Contributors [Filippo Miatto](https://github.com/ziofil), [Samuele Ferracin](https://github.com/SamFerracin), [Yuan Yao](https://github.com/sylviemonet), [Zeyue Niu](https://github.com/zeyueN) --------- Co-authored-by: Sebastián Duque Mesa <[email protected]> Co-authored-by: JacobHast <[email protected]> Co-authored-by: elib20 <[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: SamFerracin <[email protected]> Co-authored-by: Yuan Yao <[email protected]> Co-authored-by: heltluke <[email protected]>
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