Integral Transforms in a Physics-Informed (Quantum) Neural Network setting: Applications & Use-Cases

no code implementations • 28 Jun 2022 • Niraj Kumar, Evan Philip, Vincent E. Elfving

Recently, the machine learning paradigm of Physics-Informed Neural Networks emerged with increasing popularity as a method to solve differential equations by leveraging automatic differentiation.

Quantum Extremal Learning

no code implementations • 5 May 2022 • Savvas Varsamopoulos, Evan Philip, Herman W. T. van Vlijmen, Sairam Menon, Ann Vos, Natalia Dyubankova, Bert Torfs, Anthony Rowe, Vincent E. Elfving

The algorithm, called quantum extremal learning (QEL), consists of a parametric quantum circuit that is variationally trained to model data input-output relationships and where a trainable quantum feature map, that encodes the input data, is analytically differentiated in order to find the coordinate that extremizes the model.

Quantum Machine Learning