Among the many computational challenges faced across different disciplines, quantum-
mechanical systems pose some of the hardest ones and offer a natural playground for the …

The promised industrial applications of quantum computers often rest on their anticipated
ability to perform accurate, efficient quantum chemical calculations. Computational drug …

The ability to perform entangling quantum operations with low error rates in a scalable
fashion is a central element of useful quantum information processing. Neutral-atom arrays …

Computational models are an essential tool for the design, characterization, and discovery
of novel materials. Computationally hard tasks in materials science stretch the limits of …

Under suitable assumptions, some recently developed quantum algorithms can estimate the
ground-state energy and prepare the ground state of a quantum Hamiltonian with near …

In this perspective, the various measures of electron correlation used in wave function
theory, density functional theory and quantum information theory are briefly reviewed. We …

Estimating expectation values is a key subroutine in quantum algorithms. Near-term
implementations face two major challenges: a limited number of samples required to learn a …

It is expected that quantum computers would enable solving various problems that are
beyond the capabilities of the most powerful current supercomputers, which are based on …

The phenomena of quantum criticality underlie many novel collective phenomena found in
condensed matter systems. They present a challenge for classical and quantum simulation …

In this work, we propose a quantum unitary downfolding formalism based on the driven
similarity renormalization group (QDSRG) that may be combined with quantum algorithms …