Reliability-based design optimization (RBDO) is an active field of research with an ever
increasing number of contributions. Numerous methods have been proposed for the solution …

This paper reviews the state-of-the-art in robust design optimization–the search for designs
and solutions which are immune with respect to production tolerances, parameter drifts …

Modern quantum machine learning (QML) methods involve variationally optimizing a
parameterized quantum circuit on a training data set, and subsequently making predictions …

Quantum computers are promising for simulations of chemical and physical systems, but the
limited capabilities of today's quantum processors permit only small, and often approximate …

Abstract Machine learning is a promising application of quantum computing, but challenges
remain for implementation today because near-term devices have a limited number of …

With the surge of large-scale pre-trained models (PTMs), fine-tuning these models to
numerous downstream tasks becomes a crucial problem. Consequently, parameter efficient …

The famous, yet unsolved, Fermi-Hubbard model for strongly-correlated electronic systems
is a prominent target for quantum computers. However, accurately representing the Fermi …

As deep neural networks (DNNs) revolutionize machine learning, energy consumption and
throughput are emerging as fundamental limitations of CMOS electronics. This has …

Abstract The Fermi-Hubbard model is of fundamental importance in condensed-matter
physics, yet is extremely challenging to solve numerically. Finding the ground state of the …

Optimization is one of the research areas where quantum computing could bring significant
benefits. In this scenario, a hybrid quantum–classical variational algorithm, the Quantum …