Quantum-based Computational Fluid Dynamics with Quantum Circuit Learning

Project information

A powerful example of variational quantum algorithms is the so-called quantum circuit learning algorithm (QCL), which approximates functions and can solve non-linear differential equations by using the parameter shift rule. This demonstrator aims to explain the basics of QCL and uses examples to show how different functions can be approximated and differential equations can be solved.

23 Commits
1 Branch
0 Tags

README
Apache License 2.0

Created on

March 07, 2024

Copy SSH clone URLgit@gitlab.cc-asp.fraunhofer.de:fraunhofer_iao_qc/sequoia_end-to-end/quantum-based-computational-fluid-dynamics-with-quantum-circuit-learning.git
Copy HTTPS clone URLhttps://gitlab.cc-asp.fraunhofer.de/fraunhofer_iao_qc/sequoia_end-to-end/quantum-based-computational-fluid-dynamics-with-quantum-circuit-learning.git

Loading

Datenschutz und Impressum