Theory for Equivariant Quantum Neural Networks

Understand

Quantum neural network architectures that have little-to-no inductive biases are known to face trainability and generalization issues.

• Inspired by a similar problem, recent breakthroughs in machine learning address this challenge by creating models encoding the symmetries of the learning task.
• This is materialized through the usage of equivariant neural networks whose action commutes with that of the symmetry.
• In this work, we import these ideas to the quantum realm by presenting a comprehensive theoretical framework to design equivariant quantum neural networks (EQNN) for essentially any relevant symmetry group.