Neural Networks on Noisy Intermediate Scale Quantum Computers

Relatore:  Daniele Bajoni - Università di Pavia
  venerdì 18 dicembre 2020 alle ore 15.00
We present a memory-efficient quantum algorithm implementing the action of an artificial neuron according to a classical model of the perceptron with both binary and continuous variables on a quantum computer. Then we show that this model is amenable to be extended to a multilayered artificial neural network, which is able to solve tasks that would be impossible to a single one of its constituent artificial neurons. We discuss how the scalar product operation can be efficiently obtained in quantum circuits, thus laying the basis for a fully quantum artificial intelligence algorithm run on noisy intermediate-scale quantum hardware.
The algorithm, tested on noisy IBM-Q superconducting real quantum processors, succeeds in elementary classification 
and image-recognition tasks through a hybrid quantum-classical training procedure. 

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Contact Person: Alessandra Di Pierro
Titolo Formato  (Lingua, Dimensione, Data pubblicazione)
QML Seminar  pdfpdf (en, 81 KB, 07/12/20)


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Data pubblicazione
7 dicembre 2020

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