In this short review article, we aim to provide physicists not working within the quantum
computing community a hopefully easy-to-read introduction to the state of the art in the field …

To harness the potential of noisy intermediate-scale quantum devices, it is paramount to find
the best type of circuits to run hybrid quantum-classical algorithms. Key candidates are …

Real-and imaginary-time quantum state evolutions are crucial in physics and chemistry for
exploring quantum dynamics, preparing ground states, and computing thermodynamic …

Quantum subspace methods (QSMs) are a class of quantum computing algorithms where
the time-independent Schrödinger equation for a quantum system is projected onto a …

Variational quantum algorithms (VQAs) promise efficient use of near-term quantum
computers. However, training VQAs often requires an extensive amount of time and suffers …

We present an algorithm to compute Green's functions on quantum computers for interacting
electron systems, which is a challenging task on conventional computers. It uses a …

Quantum subspace diagonalization (QSD) algorithms have emerged as a competitive family
of algorithms that avoid many of the optimization pitfalls associated with parameterized …

Semidefinite programs (SDPs) are convex optimization programs with vast applications in
control theory, quantum information, combinatorial optimization, and operational research …

The quantum simulation of entangled spin systems can play a central role in quantum
magnetic materials discovery. Additionally, the simulation of spectroscopic signatures, such …

Finding the transient and steady state properties of open quantum systems is a central
problem in various fields of quantum technologies. Here, we present a quantum-assisted …