Quantum learning (in metrology and machine learning) involves estimating unknown parameters from measurements of quantum states. The quantum Fisher information matrix can bound the average amount of information learnt about the unknown parameters per experimental trial. In several scenarios, it is advantageous to concentrate information in as few states as possible. Here, we present two go-go theorems proving that negativity, a narrower nonclassicality concept than noncommutation, enables unbounded and lossless distillation of Fisher information about multiple parameters in quantum learning.
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