Description

2014/10/09, Dr. Gael Sentis Herrera (Universidad Autonoma de Barcelona)

Place: Seminar Room of Theoretical Physics Department
Time: 11h45
Title: Optimal learning of qubits does not require a quantum memory 
 
 
Abstract

A quantum learning machine for binary classification of qubit states that does not require quantum memory is introduced and shown to perform with the minimum error rate allowed by quantum mechanics for any size of
the training set. This result is shown to be robust under (an arbitrary amount of) noise and under (statistical)
variations in the composition of the training set, provided it is large enough. This machine can be used an arbitrary number of times without retraining. Its required classical memory grows only logarithmically with the number of
training qubits, while its excess risk decreases as the inverse of this number, and twice as fast as the excess
risk of an estimate-and-discriminate machine, which estimates the states of the training qubits and classifies
the data qubit with a discrimination protocol tailored to the obtained estimates.