Quantum physics has changed the way we understand our environment, and one of its
branches, quantum mechanics, has demonstrated accurate and consistent theoretical …

We use the Lipkin-Meshkov-Glick (LMG) model and the valence-space nuclear shell model
to examine the likely performance of variational quantum eigensolvers in nuclear-structure …

Excitement about the promise of quantum computers is tempered by the reality that the
hardware remains exceptionally fragile and error prone, forming a bottleneck in the …

The traveling salesman problem (TSP) is one of the most often-used NP-hard problems in
computer science to study the effectiveness of computing models and hardware platforms. In …

As restricted quantum computers become available, research focuses on finding meaningful
applications. For example, in quantum machine learning, a special type of quantum circuit …

Combinatorial problems are formulated to find optimal designs within a fixed set of
constraints and are commonly found across diverse engineering and scientific domains …

Nuclear quantum effects such as zero-point energy and hydrogen tunneling play a central
role in many biological and chemical processes. The nuclear-electronic orbital (NEO) …

Quantum software frameworks provide software engineers with the tools to study quantum
algorithms as applied to practical problems. We implement classical hash functions MD5 …

Cardiovascular diseases are among the major health problems that are likely to benefit from
promising developments in quantum machine learning for medical imaging. The chest X-ray …

Exploiting the properties of quantum information to the benefit of machine learning models is
perhaps the most active field of research in quantum computation. This interest has …