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Evaluating Artificial Neural Networks and Quantum Computing for Mechanics
Author(s) -
Mielke André,
Ricken Tim
Publication year - 2019
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.201900470
Subject(s) - computer science , artificial neural network , quantum computer , computation , quantum , artificial intelligence , scale (ratio) , computational science , theoretical computer science , machine learning , algorithm , physics , quantum mechanics
Abstract The popularization of Machine Learning (ML) and the advent of Noisy Intermediate‐Scale Quantum (NISQ) devices for Quantum Computing (QC) sparked new inspiration for the search for techniques reducing computation time in mechanics. We evaluate artificial neural networks (ANNs) as candidates for creating computationally fast surrogate models for otherwise time‐consuming simulations, using a multiscale and multiphase model describing processes in the human liver. We also give a short overview of interesting quantum‐enhanced algorithms capable of reducing computational cost in parts of complex simulations.