A review on quantum approximate optimization algorithm and its variants

K Blekos, D Brand, A Ceschini, CH Chou, RH Li… - Physics Reports, 2024‏ - Elsevier
Abstract The Quantum Approximate Optimization Algorithm (QAOA) is a highly promising
variational quantum algorithm that aims to solve combinatorial optimization problems that …

Quantum computing for high-energy physics: State of the art and challenges

A Di Meglio, K Jansen, I Tavernelli, C Alexandrou… - PRX Quantum, 2024‏ - APS
Quantum computers offer an intriguing path for a paradigmatic change of computing in the
natural sciences and beyond, with the potential for achieving a so-called quantum …

[HTML][HTML] The variational quantum eigensolver: a review of methods and best practices

J Tilly, H Chen, S Cao, D Picozzi, K Setia, Y Li, E Grant… - Physics Reports, 2022‏ - Elsevier
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 …

Quantum variational algorithms are swamped with traps

ER Anschuetz, BT Kiani - Nature Communications, 2022‏ - nature.com
One of the most important properties of classical neural networks is how surprisingly
trainable they are, though their training algorithms typically rely on optimizing complicated …

Variational quantum algorithms

M Cerezo, A Arrasmith, R Babbush… - Nature Reviews …, 2021‏ - nature.com
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 …

The power of quantum neural networks

A Abbas, D Sutter, C Zoufal, A Lucchi, A Figalli… - Nature Computational …, 2021‏ - nature.com
It is unknown whether near-term quantum computers are advantageous for machine
learning tasks. In this work we address this question by trying to understand how powerful …

Power of data in quantum machine learning

HY Huang, M Broughton, M Mohseni… - Nature …, 2021‏ - nature.com
The use of quantum computing for machine learning is among the most exciting prospective
applications of quantum technologies. However, machine learning tasks where data is …

Connecting ansatz expressibility to gradient magnitudes and barren plateaus

Z Holmes, K Sharma, M Cerezo, PJ Coles - PRX quantum, 2022‏ - APS
Parametrized quantum circuits serve as ansatze for solving variational problems and
provide a flexible paradigm for the programming of near-term quantum computers. Ideally …

Noise-induced barren plateaus in variational quantum algorithms

S Wang, E Fontana, M Cerezo, K Sharma… - Nature …, 2021‏ - nature.com
Abstract Variational Quantum Algorithms (VQAs) may be a path to quantum advantage on
Noisy Intermediate-Scale Quantum (NISQ) computers. A natural question is whether noise …

Neutral atom quantum computing hardware: performance and end-user perspective

K Wintersperger, F Dommert, T Ehmer… - EPJ Quantum …, 2023‏ - epjqt.epj.org
We present an industrial end-user perspective on the current state of quantum computing
hardware for one specific technological approach, the neutral atom platform. Our aim is to …