A quantum analytical Adam descent through parameter shift rule using Qibo
Quantum Physics
2022-10-21 v1 High Energy Physics - Phenomenology
Abstract
In this proceedings we present quantum machine learning optimization experiments using stochastic gradient descent with the parameter shift rule algorithm. We first describe the gradient evaluation algorithm and its optimization procedure implemented using the Qibo framework. After numerically testing the implementation using quantum simulation on classical hardware, we perform successfully a full quantum hardware optimization exercise using a single superconducting qubit chip controlled by Qibo. We show results for a quantum regression model by comparing simulation to real hardware optimization.
Cite
@article{arxiv.2210.10787,
title = {A quantum analytical Adam descent through parameter shift rule using Qibo},
author = {Matteo Robbiati and Stavros Efthymiou and Andrea Pasquale and Stefano Carrazza},
journal= {arXiv preprint arXiv:2210.10787},
year = {2022}
}
Comments
6 pages, 2 figures, presented in 41st International Conference on High Energy physics - ICHEP2022