Training deep quantum neural networks
with Kerstin Beer, Dmytro Bondarenko, Terry Farrelly, Tobias J. Osborne, Robert Salzmann, and Daniel Scheiermann
Here we propose a truly quantum analogue of classical neurons, which form quantum feedforward neural networks capable of universal quantum computation. We describe the efficient training of these networks using the fidelity as a cost function, providing both classical and efficient quantum implementations.
Journal: Nature Communications 11, 808 (2020)
This article is among the Top 50 Nature Communications physics articles published in 2020!