We present a memory-efficient quantum algorithm implementing the action of an artificial neuron according to a binary-valued model of the classical perceptron. 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. Here we also show that this model is amenable to be extended to a multilayered artificial neural network, which is able to solve a task that would be impossible to a single one of its constituent artificial neurons, thus laying the basis for a fully quantum artificial intelligence algorithm run on noisy intermediate-scale quantum hardware.
An quantum algorithm for feedforward neural networks tested on existing quantum hardware
Gerace D.;MacChiavello C.;Bajoni D.
2020-01-01
Abstract
We present a memory-efficient quantum algorithm implementing the action of an artificial neuron according to a binary-valued model of the classical perceptron. 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. Here we also show that this model is amenable to be extended to a multilayered artificial neural network, which is able to solve a task that would be impossible to a single one of its constituent artificial neurons, thus laying the basis for a fully quantum artificial intelligence algorithm run on noisy intermediate-scale quantum hardware.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
