Group-Invariant Quantum Machine Learning

Understand

Quantum Machine Learning (QML) models are aimed at learning from data encoded in quantum states.

• Recently, it has been shown that models with little to no inductive biases (i.e., with no assumptions about the problem embedded in the model) are likely to have trainability and generalization issues, especially for large problem sizes.
• As such, it is fundamental to develop schemes that encode as much information as available about the problem at hand.
• In this work we present a simple, yet powerful, framework where the underlying invariances in the data are used to build QML models that, by construction, respect those symmetries.