Abstract The Quantum Approximate Optimization Algorithm (QAOA) is a highly promising
variational quantum algorithm that aims to solve combinatorial optimization problems that …

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 …

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 …

Data representation is crucial for the success of machine-learning models. In the context of
quantum machine learning with near-term quantum computers, equally important …

The quantum approximate optimization algorithm (QAOA) generates an approximate
solution to combinatorial optimization problems using a variational ansatz circuit defined by …

Abstract Variational Quantum Algorithms (VQAs) are often viewed as the best hope for near-
term quantum advantage. However, recent studies have shown that noise can severely limit …

The design of fast algorithms for combinatorial optimization greatly contributes to a plethora
of domains such as logistics, finance, and chemistry. Quantum approximate optimization …

We study the distribution over measurement outcomes of noisy random quantum circuits in
the regime of low fidelity, which corresponds to the setting where the computation …

We propose a random circuit model that attempts to capture the behavior of noisy
intermediate-scale quantum devices when used for variationally solving classical …

Approximate combinatorial optimisation has emerged as one of the most promising
application areas for quantum computers, particularly those in the near term. In this work, we …