Applications such as simulating complicated quantum systems or solving large-scale linear
algebra problems are very challenging for classical computers, owing to the extremely high …

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 …

The prospect of achieving quantum advantage with quantum neural networks (QNNs) is
exciting. Understanding how QNN properties (for example, the number of parameters M) …

Abstract Variational Quantum Algorithms (VQAs) have received considerable attention due
to their potential for achieving near-term quantum advantage. However, more work is …

During the last ten years, superconducting circuits have passed from being interesting
physical devices to becoming contenders for near-future useful and scalable quantum …

The quantum approximate optimization algorithm (QAOA) is a hybrid variational quantum-
classical algorithm that solves combinatorial optimization problems. While there is evidence …

Variational quantum algorithms are ubiquitous in applications of noisy intermediate-scale
quantum computers. Due to the structure of conventional parametrized quantum gates, the …

Variational quantum algorithms rely on gradient based optimization to iteratively minimize a
cost function evaluated by measuring output (s) of a quantum processor. A barren plateau is …

We compare the bfgs optimizer, adam and NatGrad in the context of vqes. We systematically
analyze their performance on the qaoa ansatz for the transverse field Ising and the XXZ …

The quantum approximate optimization algorithm (QAOA) has become a cornerstone of
contemporary quantum applications development. In QAOA, a quantum circuit is trained—by …