0.5.0

Added:

• A new experimental lambeq.experimental.discocirc module that contains an efficient lambeq.experimental.discocirc.DisCoCircReader and all the required functionality for converting long texts and entire multi-paged documents into quantum circuits, based on the DisCoCirc framework.
• A new tree representation of a pregroup diagram, termed pregroup tree, is implemented through the lambeq.text2diagram.pregroup_tree.PregroupTreeNode class. This lays the groundwork for drastically improving the parsing and internal processing of diagrams.
• A new experimental end-to-end parser class, lambeq.text2diagram.OncillaParser, that simplifies the process of generating diagrams from text, minimizing or even eliminating exposure of the user to CCG representations and functionality. This parser utilises the pregroup tree representation of diagrams. This does not replace BobcatParser as the default parser.
• A new lambeq.backend.grammar.Frame data structure that allows the recursive grouping of lambeq boxes and diagrams and can be seen as a quantum supermap acting on the enclosed arguments. Frames are used in DisCoCirc diagrams.
• A new lambeq.training.PytorchQuantumModel class that allows Pytorch autograd to be used on quantum circuits, while so far it was possible to use it only on tensor networks (credit: Kin Ian Lo).
• A new native lambeq.backend.symbol.Symbol class that eliminates any dependencies with SymPy and improves efficiency.
• A new rewrite rule class, lambeq.rewrite.CollapseDomainRewriteRule, that converts boxes into domain-less boxes by uncurrying (credit: Kin Ian Lo).
• New lambeq.backend.Diagram.remove_snakes and lambeq.backend.Diagram.rigid_normal_form methods that make the specific rewrites also available outside of the original lambeq.backend.Diagram.normal_form method (credit: Kin Ian Lo).
• Caching options for fast access to already computed tensor contraction paths for tensor network models, specifically PytorchModel and PytorchQuantumModel. The constructor of these models now takes a tn_path_optimizer argument, which can be a TnPathOptimizer object, replicating the old un-cached behaviour, or a CachedTnPathOptimizer which allows caching of the computed tensor contraction paths for quick lookup.
• Support for evaluating mixed-scalar PennyLane circuits, i.e. circuits where all qubits are either discarded or post-selected.
• Two new ansätze from the Sim et al. paper (arXiv:1905.10876), Sim9Ansatz and Sim9CxAnsatz.
• Support for ancilla qubits in lambeq's ansätze.

Changed:

• Significantly improved the efficiency of the PennyLaneModel.
• Refactored all models so that they do not depend on tket as an intermediate step for their conversions.
• CircuitAnsatz now acts as a dagger functor (credit: Kin Ian Lo).
• Refactored QuantumModel to be less numpy-specific and easier to extend with other backends.
• Made the split tensor ansätze, i.e. SpiderAnsatz and MPSAnsatz, work on boxes with domains. This utilises the newly-implemented CollapseDomainRewriteRule (credit: Kin Ian Lo).
• Changed the device keyword argument for model-based parsers, e.g. BobcatParser, so that it follows PyTorch convention and supports multiple types.
• Added the new lambeq.text2diagram.OncillaParser as a parser option to the CLI via the -p oncilla argument.
• Removed the deprecated lambeq.text2diagram.DepCCGParser as a parser option from the CLI.
• Refactored tokeniser loading from SpacyTokeniser into a new utility function lambeq.core.utils.get_spacy_tokeniser.
• Significantly extended and restructured the documentation pages, fixed various issues, and added more material and tutorials.
• Made tket an optional dependency.

Fixed:

• Fixed an enum incompatibility with Python > 3.10.
• Fixed the behaviour of tensoring a type with the identity diagram.
• Fixed a lambeq.backend.Diagram.lambdify method error when used with a daggered tensor box (credit: Kin Ian Lo).

0.4.3

Changed:

• Documentation has now been moved to a dedicated repository and got a new URL (https://cqcl.github.io/lambeq-docs/).
• Changed the landing page and some visuals in the online documentation.
• Updated README to reflect the new docs structure.

Fixed:

• Fixed minor issues on some documentation pages and the README file.

0.4.2

Added:

• Added timing information to training logs and model checkpoints.

Changed:

• Changed theme of online documentation.
• Updated required version of pytket to 1.31.0.

Fixed:

• Fixed bug in generation of single-legged quantum spiders.
• Fixed bug when evaluating quantum circuits using Tket.

Removed:

• Removed support for Python 3.9.

0.4.1

Added:

• Support for Python 3.12.
• A new Sim4Ansatz based on the paper by Sim et al. (arXiv:1905.10876).
• A new argument in Trainer.fit for specifying an early_stopping_criterion other than validation loss.
• A new argument collapse_noun_phrases in methods of CCGParser and CCGTree classes (for example, see CCGParser.sentence2diagram) that allows the user to maintain noun phrases in the derivation or collapse them into nouns as desired.
• Raised meaningful exception when users try to convert to/from DisCoPy 1.1.0.

Changed:

• An internal refactoring of module backend.drawing in view of planned new features.
• Updated random number generation in TketModel by using the recommended numpy.random.default_rnd method.

Fixed:

• Handling of possible empty Bra s and Ket s during conversion from DisCoPy.
• Fixed a bug in JIT compilation of mixed circuit evaluations.

0.4.0

Added:

• A new integrated backend that replaces DisCoPy, which until now was providing the low-level functionality of lambeq. The new backend offers better performance, increased stability, faster training speeds, and a simplified high-level interface to the user. The new backend consists of the following sub-modules:

  • lambeq.backend.grammar: Contains the building blocks for creating string diagrams.
  • lambeq.backend.tensor: Contains the necessary classes to create tensor diagrams.
  • lambeq.backend.quantum: Adds quantum-specific functionality to the backend and provides a circuit simulator based on the TensorNetwork library.
  • lambeq.backend.pennylane: Interface with PennyLane.
  • lambeq.backend.tk: Inteface with Tket.
  • lambeq.backend.numerical_backend: Common interface for numerical backends (such as Numpy, Jax, PyTorch, TensorFlow)
  • lambeq.backend.drawing: Contains drawing functionality for diagrams and circuits.

• lambeq.BobcatParser: Added a special case for adjectival conjunction in tree translation.

• lambeq.TreeReader: Diagrams now are created straight from the lambeq.CCGTree.

• lambeq.CCGRule apply method: Added lambeq.CCGRule.apply method to class lambeq.CCGRule.

Changed:

• Diagram-level rewriters: Rewrite functions remove_cups and remove_swaps are now refactored as diagram-level rewriters, lambeq.RemoveCupsRewriter and lambeq.RemoveSwapsRewriter correspondingly.
• Extra whitespace is now ignored in the lambeq.Tokeniser.

Fixed:

• lambeq.UnknownWordsRewriteRule: Fixed rewriting of non-word boxes.

Removed:

• Removed CCGTree.to_biclosed_diagram and references to discopy.biclosed. Now CCG trees are directly converted into string diagrams, without the extra step of storing the derivation in a biclosed form.
• lambeq.CCGRule: Removed replace_cat_result and added lambeq.CCGRule.resolve.

0.3.3

This update features contributions from participants in unitaryHACK 2023:

• Two new optimisers:
  • The Nelder-Mead optimiser. (credit: Gopal Dahale)
  • The Rotosolve optimiser. (credit: Ahmed Darwish)
• A new rewrite rule for handling unknown words. (credit: WingCode)

Many thanks to all who participated.

This update also contains the following changes:

Added:

• DiagramRewriter is a new class that rewrites diagrams by looking at the diagram as a whole rather than by using rewrite rules on individual boxes. This includes an example UnifyCodomainRewriter which adds an extra box to the end of diagrams to change the output to a specified type. (credit: A.C.E07)
• Added an early stopping mechanism to Trainer using the parameter early_stopping_interval.

Fixed:

• In PennyLaneModel, SymPy symbols are now substituted during the forward pass so that gradients are back-propagated to the original parameters.
• A pickling error that prevented CCG trees produced by BobcatParser from being unpickled has been fixed.

0.3.2

Added:

• Support for DisCoPy >= 1.1.4 (credit: toumix).
  • replaced discopy.rigid with discopy.grammar.pregroup everywhere.
  • replaced discopy.biclosed with discopy.grammar.categorial everywhere.
  • Use Diagram.decode to account for the change in contructor signature Diagram(inside, dom, cod).
  • updated attribute names that were previously hidden, e.g. ._data becomes .data.
  • replaced diagrammatic conjugate with transpose.
  • swapped left and right currying.
  • dropped support for legacy DisCoPy.
• Added CCGType class for utilisation in the biclosed_type attribute of CCGTree, allowing conversion to and from a discopy categorial object using CCGType.discopy and CCGType.from_discopy methods.
• CCGTree: added reference to the original tree from parsing by introducing a metadata field.

Changed:

• Internalised DisCoPy quantum ansätze in lambeq.
• IQPAnsatz now ends with a layer of Hadamard gates in the multi-qubit case and the post-selection basis is set to be the computational basis (Pauli Z).

Fixed:

• Fixed a bottleneck during the initialisation of the PennyLaneModel caused by the inefficient substitution of Sympy symbols in the circuits.
• Escape special characters in box labels for symbol creation.
• Documentation: fixed broken links to DisCoPy documentation.
• Documentation: enabled sphinxcontrib.jquery extension for Read the Docs theme.
• Fixed disentangling RealAnsatz in extend-lambeq tutorial notebook.
• Fixed model loading in PennyLane notebooks.
• Fixed typo SPSAOptimizer (credit: Gopal-Dahale)

Removed:

• Removed support for Python 3.8.

0.3.1

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.

0.3.0

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 theQuantumTrainer. 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.

0.2.8

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.