The variational quantum eigensolver (or VQE), first developed by Peruzzo et al.(2014), has
received significant attention from the research community in recent years. It uses the …

Discoveries in quantum materials, which are characterized by the strongly quantum-
mechanical nature of electrons and atoms, have revealed exotic properties that arise from …

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 …

Parameterized quantum circuits are a promising technology for achieving a quantum
advantage. An important application is the variational simulation of time evolution of …

Quantum computers hold promise to enable efficient simulations of the properties of
molecules and materials; however, at present they only permit ab initio calculations of a few …

Laser-cooled and trapped atomic ions form an ideal standard for the simulation of interacting
quantum spin models. Effective spins are represented by appropriate internal energy levels …

Quantum computing technologies are making steady progress. This has opened new
opportunities for tackling problems whose complexity prevents their description on classical …

We simulate the effects of different types of noise in state preparation circuits of variational
quantum algorithms. We first use a variational quantum eigensolver to find the ground state …

We investigate fully self-consistent multiscale quantum-classical algorithms on current
generation superconducting quantum computers, in a unified approach to tackle the …

We propose a class of randomized quantum algorithms for the task of sampling from matrix
functions, without the use of quantum block encodings or any other coherent oracle access …