Examples

• Tokenisation
  • Word tokenisation
  • Splitting a document into sentences
• Handling unknown words
  • Handling unknown words in syntax-free models
  • Handling unknown words in syntax-based models
• Parser
• Reader
• Tree reader
• Rewrite
  • Auxiliary rule
  • Connector rule
  • Determiner rule
  • Adverb rules
  • Prepositional phrase rule
  • Relative Pronoun rules
  • Coordination
  • Remove cups
  • Curry functor
• Circuit
• Tensor
• Rotosolve optimizer
  • Create diagram
  • Create circuits
  • Parameterise
  • Define evaluation metric
  • Initialize trainer
  • Show results
  • Final output
• Classical pipeline
  • Input data
  • Create diagrams
  • Create circuits
  • Parameterise
  • Define Evaluation Metric
  • Initialize Trainer
  • Train
  • Show results
• Quantum pipeline using the Quantum Trainer
  • Read in the data and create diagrams
  • Create diagrams
  • Remove the cups
  • Create circuits
  • Parameterise
  • Define evaluation metric
  • Initialize trainer
  • Train
  • Show results
• Quantum pipeline using JAX backend
  • Read in the data and create diagrams
  • Create diagrams
  • Remove the cups
  • Create circuits
  • Parameterise
  • Define evaluation metric
  • Initialize trainer
  • Train
  • Show results
• Training hybrid models using the Pennylane backend
  • Read in the data and create diagrams
  • Remove cups
  • Create circuits
  • Create (pure quantum) model and initialise parameters
  • Prepare train dataset
  • Training
    • Using PytorchTrainer
    • Determine test accuracy
    • Using standard PyTorch
  • Determine the test accuracy
  • Creating a hybrid model
  • Make paired dataset
  • Initialise the model
  • Train the model and log accuracies