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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.
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