Środowiskowe Seminarium z Informacji i Technologii Kwantowych
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2021-06-17 (11:15) 
David Arvidsson Shukur (University of Cambridge)
Quantum Learnability is Arbitrarily Distillable
Zoom link: https://zoom.us/j/6526721604?pwd=Y0pPdE9vT1hNWWNiZVBMaEVOeHN2dz09
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. In this talk, I will present a “go-go” theorem proving the possibility of unbounded and lossless distillation of Fisher information about multiple parameters in quantum learning. Our results enable the construction of filters that can reduce arbitrarily the quantum-state intensity on experimental detectors, whilst retaining all initial information. I will show that the quantum resource that enables this is negativity, a narrower nonclassicality concept than noncommutation.
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. In this talk, I will present a “go-go” theorem proving the possibility of unbounded and lossless distillation of Fisher information about multiple parameters in quantum learning. Our results enable the construction of filters that can reduce arbitrarily the quantum-state intensity on experimental detectors, whilst retaining all initial information. I will show that the quantum resource that enables this is negativity, a narrower nonclassicality concept than noncommutation.