Information-theoretic generalization bounds for learning from quantum data

Understand

Learning tasks play an increasingly prominent role in quantum information and computation.

• They range from fundamental problems such as state discrimination and metrology over the framework of quantum probably approximately correct (PAC) learning, to the recently proposed shadow variants of state tomography.
• However, the many directions of quantum learning theory have so far evolved separately.
• We propose a general mathematical formalism for describing quantum learning by training on classical-quantum data and then testing how well the learned hypothesis generalizes to new data.