Lambeq

Changed:

- Added example and tutorial notebooks to tests.
- Dependencies: pinned the maximum version of Jax and Jaxlib to 0.4.6 to avoid a JIT-compilation error when using the `NumpyModel`.

Fixed:

- Documentation: fixed broken DisCoPy links.
- Fixed PyTorch datatype errors in example and tutorial notebooks.
- Updated custom ansätze in tutorial notebook to match new structure of `CircuitAnsatz` and `TensorAnsatz`.

Added:

- Support for hybrid quantum-classical models using the `PennyLaneModel`. `PennyLane` is a powerful QML library that allows the development of hybrid ML models by hooking numerically determined gradients of parametrised quantum circuits (PQCs) to the autograd modules of ML libraries like PyTorch or TensorFlow.
- Add lambeq-native loss functions `LossFunction` to be used in conjunction with the`QuantumTrainer`. Currently, we support the `CrossEntropyLoss`, `BinaryCrossEntropyLoss`, and the `MSELoss` loss functions.
- Python 3.11 support.
- An extensive NLP-101 tutorial, covering basic definitions, text preprocessing, tokenisation, handling of unknown words, machine learning best practices, text classification, and other concepts.

Changed:

- Improve tensor initialisation in the `PytorchModel`. This enables the training of larger models as all parameters are initialised such that the expected L2 norm of all output vectors is approximately 1. We use a symmetric uniform distribution where the range depends on the output dimension (flow) of each box.
- Improve the fail-safety of the `BobcatParser` model download method by adding hash checks and atomic transactions.
- Use type union expression `|` instead of `Union` in type hints.
- Use `raise from` syntax for better exception handling.
- Update the requirements for the documentation.

Fixed:

- Fixed bug in `SPSAOptimizer` triggered by the usage of masked arrays.
- Fixed test for `NumpyModel` that was failing due to a change in the behaviour of Jax.
- Fixed brittle quote-wrapped strings in error messages.
- Fixed 400 response code during Bobcat model download.
- Fixed bug where `CircuitAnsatz` would add empty discards and postselections to the circuit.

Removed:

- Removed install script due to deprecation.

Changed:
- Improved the performance of `NumpyModel` when using Jax JIT-compilation.
- Dependencies: pinned the required version of DisCoPy to 0.5.X.

Fixed:
- Fixed incorrectly scaled validation loss in progress bar during model training.
- Fixed symbol type mismatch in the quantum models when a circuit was previously converted to tket.

Added:
- Added support for Japanese to `DepCCGParser`. (credit: KentaroAOKI https://github.com/CQCL/lambeq/pull/24)
- Overhauled the `CircuitAnsatz` interface, and added three new ansätze.
- Added helper methods to `CCGTree` to get the children of a tree.
Added a new `.tree2diagram` method to `TreeReader`, extracted from `TreeReader.sentence2diagram`.
- Added a new `TreeReaderMode` named `HEIGHT`.
- Added new methods to `Checkpoint` for creating, saving and loading checkpoints for training.
- Documentation: added a section for how to select the right model and trainer for training.
- Documentation: added links to glossary terms throughout the documentation.
- Documentation: added UML class diagrams for the sub-packages in lambeq.

Changed:
- Dependencies: bumped the minimum versions of `discopy` and `torch`.
- `IQPAnsatz` now post-selects in the Hadamard basis.
- `PytorchModel` now initialises using `xavier_uniform`.
- `CCGTree.to_json` can now be applied to `None`, returning `None`.
- Several slow imports have been deferred, making lambeq much faster to import for the first time.
- In `CCGRule.infer_rule`, direction checks have been made explicit.
- `UnarySwap` is now specified to be a `unaryBoxConstructor`.
- `BobcatParser` has been refactored for easier use with external evaluation tools.
- Documentation: headings have been organised in the tutorials into subsections.

Fixed:
- Fixed how `CCGRule.infer_rule` assigns a `punc + X` instance: if the result is `X\X` the assigned rule is `CONJUNCTION`, otherwise the rule is `REMOVE_PUNCTUATION_LEFT` (similarly for punctuation on the right).

Removed:
- Removed unnecessary override of `.from_diagrams` in `NumpyModel`.
- Removed unnecessary `kwargs` parameters from several constructors.
- Removed unused `special_cases` parameter and `_ob` method from `CircuitAnsatz`.

Added:
- A strict pregroups mode to the CLI. With this mode enabled, all swaps are removed from the output string diagrams by changing the ordering of the atomic types, converting them into a valid pregroup form.

Fixed:
- Adjusted the behaviour of output normalisation in quantum models. Now, `NumpyModel` always returns probabilities instead of amplitudes.
- Removed the prediction from the output of the `SPSAOptimizer`, which now returns just the loss.

Added:
- Added a "swapping" unary rule box to handle unary rules that change the direction of composition, improving the coverage of the `BobcatParser`.
- Added a `--version` flag to the CLI.
- Added a `make_checkpoint` method to all training models.

Changed:
- Changed the `WebParser` so that the online service to use is specified by name rather than by URL.
- Changed the `BobcatParser` to only allow one tree per category in a cell, doubling parsing speed without affecting the structure of the parse trees (in most cases).
- Made the linting of the codebase stricter, enforced by the GitHub action. The flake8 configuration can be viewed in the `setup.cfg` file.

Fixed:
- Fixed the parameter names in `CCGRule`, where `dom` and `cod` had inadvertently been swapped.