Release notes



  • 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.


  • 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.



  • 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.


  • 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 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 install script due to deprecation.



  • Improved the performance of NumpyModel when using Jax JIT-compilation.

  • Dependencies: pinned the required version of DisCoPy to 0.5.X.


  • 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.




  • 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.




  • 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 as given in [Lam1999].

  • 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 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 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).

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

  • Made the linting of the codebase stricter, enforced by the GitHub action. The flake8 configuration can be viewed in the setup.cfg file.


  • Fix a bug that caused the BobcatParser and the WebParser to trigger an SSL certificate error using Windows.

  • Fix false positives in assigning conjunction rule using the CCGBankParser. The rule , + X[conj] -> X[conj] is a case of removing left punctuation, but was being assigned conjunction erroneously.

  • Add support for using jax as backend of tensornetwork when setting use_jit=True in the NumpyModel. The interface is not affected by this change, but performance of the model is significantly improved.


  • Fix a bug that raised a dtype error when using the TketModel on Windows.

  • Fix a bug that caused the normalisation of scalar outputs of circuits without open wires using a QuantumModel.

  • Change the behaviour of spiders_reader such that the spiders decompose logarithmically. This change also affects other rewrite rules that use spiders, such as coordination and relative pronouns.


  • CCGRule: Add symbol() method that returns the ASCII symbol of a given CCG rule.

  • CCGTree: Extend deriv() method with CCG output. It is now capable of returning standard CCG diagrams.

  • Command-line interface: Add CCG mode. When enabled, the output will be a string representation of the CCG diagram corresponding to the CCGTree object produced by the parser, instead of a DisCoPy diagram or circuit.

  • Documentation: Add a troubleshooting page.


  • Add support for Python 3.10.

  • Unify class hierarchies for parsers and readers: CCGParser is now a subclass of Reader and placed in the common package text2diagram. The old packages reader and ccg2discocat are no longer available. Compatibility problems with previous versions should be minimal, since from Release 0.2.0 and onwards all lambeq classes can be imported from the global namespace.

  • Add CurryRewriteRule, which uses map-state duality in order to remove adjoint types from the boxes of a diagram. When used in conjunction with normal_form(), this removes cups from the diagram, eliminating post-selection.

  • The Bobcat parser now updates automatically when new versions are made available online.

  • Update grammar file of Bobcat parser to avoid problems with conflicting unary rules.

  • Allow customising available root categories for the parser when using the command-line interface.


  • A new Checkpoint class that implements pickling and file operations from the Trainer and Model.

  • Improvements to the training module, allowing multiple diagrams to be accepted as input to the SPSAOptimizer.

  • Updated documentation, including sub-package structures and class diagrams.


  • A new state-of-the-art CCG parser based on [SC2021], fully integrated with lambeq, which replaces depccg as the default parser of the toolkit. The new Bobcat parser has better performance, simplifies installation, and provides compatibility with Windows (which was not supported due to a depccg conflict). depccg is still supported as an alternative external dependency.

  • A training package, providing a selection of trainers, models, and optimizers that greatly simplify supervised training for most of lambeq’s use cases, classical and quantum. The new package adds several new features to lambeq, such as the ability to save to and restore models from checkpoints.

  • Furthermore, the training package uses DisCoPy’s tensor network capability to contract tensor diagrams efficiently. In particular, DisCoPy 0.4.1’s new unitary and density matrix simulators result in substantially faster training speeds compared to the previous version.

  • A command-line interface, which provides most of lambeq’s functionality from the command line. For example, lambeq can now be used as a standard command-line pregroup parser.

  • A web parser class that can send parsing queries to an online API, so that local installation of a parser is not strictly necessary anymore. The web parser is particularly helpful for testing purposes, interactive usage or when a local parser is unavailable, but should not be used for serious experiments.

  • A new pregroups package that provides methods for easy creation of pregroup diagrams, removal of cups, and printing of diagrams in text form (i.e. in a terminal).

  • A new TreeReader class that exploits the biclosed structure of CCG grammatical derivations.

  • Three new rewrite rules for relative pronouns [SCC2014a] [SCC2014b] and coordination [Kar2016].

  • Tokenisation features have been added in all parsers and readers.

  • Additional generator methods and minor improvements for the CCGBankParser class.

  • Improved and more detailed package structure.

  • Most classes and functions can now be imported from lambeq directly, instead of having to import from the sub-packages.

  • The circuit and tensor modules have been combined into an lambeq.ansatz package. (However, as mentioned above, the classes and functions they define can now be imported directly from lambeq and should continue to do so in future releases.)

  • Improved documentation and additional tutorials.


  • Add URLs to the setup file.

  • Fix logo link in README.

  • Fix missing version when building docs in GitHub action.

  • Fix typo in the description keyword of the setup file.


  • Update install script to use PyPI package.

  • Add badges and documentation link to the README file.

  • Add lambeq logo and documentation link to the GitHub repository.

  • Allow documentation to get the package version automatically.

  • Add keywords and classifiers to the setup file.

  • Fix: Add lambeq.circuit module to top-level lambeq package.

  • Fix references to license file.


The initial release of lambeq, containing a lot of core material. Main features:

  • Converting sentences to string diagrams.

  • CCG parsing, including reading from CCGBank.

  • Support for the depccg parser.

  • DisCoCat, bag-of-words, and word-sequence compositional models.

  • Support for adding new compositional schemes.

  • Rewriting of diagrams.

  • Ansätze for circuits and tensors, including various forms of matrix product states.

  • Support for JAX and PyTorch integration.

  • Example notebooks and documentation.