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Found 5 papers, 1 papers with code
On the Role of Entanglement and Statistics in Learning
We exhibit a class $C$ that gives an exponential separation between QSQ learning and quantum learning with entangled measurements (even in the presence of noise).
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.
Entangled Datasets for Quantum Machine Learning
For this purpose, we introduce the NTangled dataset composed of quantum states with different amounts and types of multipartite entanglement.
