Machine learning with lambeq

This guide provides a high-level introduction to lambeq’s features for supervised learning, including descriptions of the available models, backends, and trainers, as well as details about experimental use cases for quantum runs and simulations on classical computers.

• Basic concepts
• lambeq use cases
  • Exact (non shot-based) simulation of quantum circuits on classical hardware
  • Shot-based simulation of quantum circuits on classical hardware
  • Evaluation of quantum circuits on a quantum computer
  • Evaluation of classical tensor-based models
  • Hybrid classical/quantum simulations on classical hardware
• Choosing a model
  • NumpyModel
  • PennyLaneModel
  • PytorchModel
  • PytorchQuantumModel
  • TketModel

See also:

• Training tutorials