Quantum mechanics is at the heart of our technology and economy - the laser and the transistor are quantum devices - but its full potential is far from being realized. Recent technological advances in optics, nanoscience and engineering allow experimentalists to create artificial structures or put microscopic and mesoscopic systems under new manipulable conditions in which quantum phenomena play a fundamental role.

Quantum technologies exploit these effects with practical purposes. The objective of Quantum Science is to discover, study, and control quantum efects at a fundamental level. These are two sides of a virtuous circle: new technologies lead to the discovery and study of new phenomena that will lead to new technologies.

Our aim is  to control and understand quantum phenomena in a multidisciplinary intersection of  Quantum Information, Quantum optics and cold atoms, Quantum Control, Spintronics, Quantum metrology, Atom interferometry, Superconducting qubits and Circuit QED and Foundations of Quantum Mechanics.

QUINST is funded in part as a “Grupo Consolidado” from a Basque Government Grant (J. G. Muga is the current PI), and functions as a network of groups with their own funding, structure, and specific goals.  



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Dr. Gael Sentis Herrera (Universidad Autonoma de Barcelona) (Seminar)

When and where



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 

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.