RT Journal Article
T1 Speeding up quantum perceptron via shortcuts to adiabaticity
A1 Ban, Yue
A1 Chen, Xi
A1 Torrontegui Muñoz, Erik
A1 Solano, Enrique
A1 Casanova, Jorge
AB The quantum perceptron is a fundamental building block for quantum machine learning. This is a multidisciplinary field that incorporates abilities of quantum computing, such as state superposition and entanglement, to classical machine learning schemes. Motivated by the techniques of shortcuts to adiabaticity, we propose a speed-up quantum perceptron where a control field on the perceptron is inversely engineered leading to a rapid nonlinear response with a sigmoid activation function. This results in faster overall perceptron performance compared to quasi-adiabatic protocols, as well as in enhanced robustness against imperfections in the controls.
PB Nature Portfolio
SN 2045-2322
YR 2020
FD 2020-03-11
LK https://hdl.handle.net/10016/32264
UL https://hdl.handle.net/10016/32264
LA eng
NO We acknowledge financial support from Spanish Government via PGC2018-095113-B-I00 (MCIU/AEI/FEDER, UE), Basque Government via IT986-16, as well as from QMiCS (820505) and OpenSuperQ (820363) of the EU Flagship on Quantum Technologies, and the EU FET Open Grant Quromorphic. J. C. acknowledges support from the UPV/EHU grant EHUrOPE. X. C. acknowledges NSFC (11474193), SMSTC (18010500400 and 18ZR1415500), the Program for Eastern Scholar and the Ramón y Cajal program of the Spanish MINECO (RYC-2017-22482). E.T. acknowledges support from Project PGC2018-094792-B-I00 (MCIU/AEI/FEDER,UE), CSIC Research Platform PTI-001, and CAM/FEDER Project No. S2018/TCS-4342 (QUITEMAD-CM).
DS e-Archivo
RD 1 sept. 2024