In recent decades, the field of quantum computing has experienced remarkable progress.
This progress is marked by the superior performance of many quantum algorithms …

Loading functions into quantum computers represents an essential step in several quantum
algorithms, such as quantum partial differential equation solvers. Therefore, the inefficiency …

This is a review of recent research exploring and extending present-day quantum computing
capabilities for fusion energy science applications. We begin with a brief tutorial on both …

While quantum computers are naturally well suited to implementing linear operations, it is
less clear how to implement nonlinear operations on quantum computers. However …

Quantum algorithms manipulate the amplitudes of quantum states to find solutions to
computational problems. In this work, we present a framework for applying a general class of …

Generative machine learning methods such as large-language models are revolutionizing
the creation of text and images. While these models are powerful they also harness a large …

Computing nonlinear functions over multilinear forms is a general problem with applications
in risk analysis. For instance in the domain of energy economics, accurate and timely risk …

The article is devoted to the possibilities of considering the evolution of biological systems in
connection with the unique emergent properties of antenna arrays, that is, systems of …

Estimating quantum amplitudes is a fundamental task in quantum computing and serves as
a core subroutine in numerous quantum algorithms. In this work, we present a novel …

Building a quantum analog of classical deep neural networks represents a fundamental
challenge in quantum computing. A key issue is how to address the inherent nonlinearity of …