Quantum implementation of an artificial feed-forward neural network
- URL: http://arxiv.org/abs/1912.12486v1
- Date: Sat, 28 Dec 2019 16:49:19 GMT
- Title: Quantum implementation of an artificial feed-forward neural network
- Authors: Francesco Tacchino, Panagiotis Barkoutsos, Chiara Macchiavello, Ivano
Tavernelli, Dario Gerace and Daniele Bajoni
- Abstract summary: We show an experimental realization of an artificial feed-forward neural network implemented on a state-of-art superconducting quantum processor.
The network is made of quantum artificial neurons, which individually display a potential advantage in storage capacity.
We demonstrate that this network can be equivalently operated either via classical control or in a completely coherent fashion.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Artificial intelligence algorithms largely build on multi-layered neural
networks. Coping with their increasing complexity and memory requirements calls
for a paradigmatic change in the way these powerful algorithms are run. Quantum
computing promises to solve certain tasks much more efficiently than any
classical computing machine, and actual quantum processors are now becoming
available through cloud access to perform experiments and testing also outside
of research labs.
Here we show in practice an experimental realization of an artificial
feed-forward neural network implemented on a state-of-art superconducting
quantum processor using up to 7 active qubits. The network is made of quantum
artificial neurons, which individually display a potential advantage in storage
capacity with respect to their classical counterpart, and it is able to carry
out an elementary classification task which would be impossible to achieve with
a single node. We demonstrate that this network can be equivalently operated
either via classical control or in a completely coherent fashion, thus opening
the way to hybrid as well as fully quantum solutions for artificial
intelligence to be run on near-term intermediate-scale quantum hardware.
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