In this paper we explore how quantum scientific machine learning can be used to tackle the
challenge of weather modeling. Using parametrized quantum circuits as machine learning …

Parameterized quantum circuits as machine learning models are typically well described by
their representation as a partial Fourier series of the input features, with frequencies …

We propose physics-informed holomorphic neural networks (PIHNNs) as a method to solve
boundary value problems where the solution can be represented via holomorphic functions …

We present a generative model that amortises computation for the field around eg
gravitational or magnetic sources. Exact numerical calculation has either computational …

Quantum devices are being developed to perform computation in an inherently non-
classical way. These devices are fundamentally different from conventional computers and …

Abstract Learning from Demonstration (LfD) is a powerful approach that enables users to
program robots by simply demonstrating how to perform tasks. Rather than just mimicking …