Solving PDEs with Quantum Deep Neural Network#
Li Zhong (HLRS)
Abstract#
Partial Differential Equations (PDEs) are commonly used in both science and industries to model various dynamical systems. Thus, solving PDEs is one of the most important tasks which is often an exceedingly difficult due to the notoriously difficult problem known as the “curse of dimensionality.” Recent advances in deep learning and the possibility of collecting and storing massive amount of data offers us the opportunity for solving PDEs by addressing the curse of dimensionality in a new way. However, deep learning methods are often constrained by computation power and memory limit. To tackle these shortcomings, we designed a quantum neural network on Quantum Computers (QCs) to solve PDEs.