Quantum computers can in principle solve certain problems exponentially more quickly than
their classical counterparts. We have not yet reached the advent of useful quantum …

In recent years, significant advances have been made in the design and evaluation of
balanced (hyper) graph partitioning algorithms. We survey trends of the past decade in …

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 Digital Annealer (DA) is a dedicated architecture for high-speed solving of combinatorial
optimization problems mapped to an Ising model. With fully coupled bit connectivity and high …

Though neuromorphic computers have typically targeted applications in machine learning
and neuroscience ('cognitive'applications), they have many computational characteristics …

The ever increasing demand for computational power combined with the predicted plateau
for the miniaturization of existing silicon-based technologies has made the search for low …

Abstract Quantum Bridge Analytics relates generally to methods and systems for hybrid
classical-quantum computing, and more particularly is devoted to develo** tools for …

Combinatorial optimization on near-term quantum devices is a promising path to
demonstrating quantum advantage. However, the capabilities of these devices are …