01375nas a2200169 4500008003900000245004400039210004400083260003700127300000800164490000600172520087200178100001801050700002201068700002401090700001801114856007301132 2012 d00aQuantum learning without quantum memory0 aQuantum learning without quantum memory bNature Publishing Groupc10/2012 a7080 v23 aA 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.1 aSentís, Gael1 aCalsamiglia, John1 aMuñoz-Tapia, Ramon1 aBagan, Emilio uhttp://www.nature.com/srep/2012/121005/srep00708/full/srep00708.html