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

Quantum computers have demonstrable ability to solve problems at a scale beyond brute-
force classical simulation. Interest in quantum algorithms has developed in many areas …

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 …

Faster algorithms for combinatorial optimization could prove transformative for diverse areas
such as logistics, finance and machine learning. Accordingly, the possibility of quantum …

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 impressive progress in quantum hardware of the last years has raised the interest of the
quantum computing community in harvesting the computational power of such devices …

The emergence of quantum computers as a new computational paradigm has been
accompanied by speculation concerning the scope and timeline of their anticipated …

Training machine learning models on classical computers is usually a time and compute
intensive process. With Moore's law nearing its inevitable end and an ever-increasing …

A large ongoing research effort focuses on variational quantum algorithms (VQAs),
representing leading candidates to achieve computational speed-ups on current quantum …

Realizing the potential of near-term quantum computers to solve industry-relevant
constrained-optimization problems is a promising path to quantum advantage. In this work …