Search Results for author:
Found 4 papers, 0 papers with code
Theoretical Guarantees for Permutation-Equivariant Quantum Neural Networks
Despite the great promise of quantum machine learning models, there are several challenges one must overcome before unlocking their full potential.
Theory for Equivariant Quantum Neural Networks
Most currently used quantum neural network architectures have little-to-no inductive biases, leading to trainability and generalization issues.
Representation Theory for Geometric Quantum Machine Learning
Recent advances in classical machine learning have shown that creating models with inductive biases encoding the symmetries of a problem can greatly improve performance.
Quantum algorithms for group convolution, cross-correlation, and equivariant transformations
Group convolutions and cross-correlations, which are equivariant to the actions of group elements, are commonly used in mathematics to analyze or take advantage of symmetries inherent in a given problem setting.
