Publications: Quantum machine learning

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)
ArXiv: 1902.10445