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

Quantum technology has grown out of quantum information theory and now provides a
valuable tool that researchers from numerous fields can add to their toolbox of research …

Combinatorial optimization problems are pervasive across science and industry. Modern
deep learning tools are poised to solve these problems at unprecedented scales, but a …

Quantum annealing is a computing paradigm that has the ambitious goal of efficiently
solving large-scale combinatorial optimization problems of practical importance. However …

Unconventional, special-purpose machines may aid in accelerating the solution of some of
the hardest problems in computing, such as large-scale combinatorial optimizations, by …

Abstract The work of Berezinskii, Kosterlitz and Thouless in the 1970s, revealed exotic
phases of matter governed by the topological properties of low-dimensional materials such …

Optimization, sampling and machine learning are topics of broad interest that have inspired
significant developments and new approaches in quantum computing. One such approach …

Understanding magnetic phases in quantum mechanical systems is one of the essential
goals in condensed matter physics, and the advent of prototype quantum simulation …

Adiabatic quantum computing (AQC) started as an approach to solving optimization
problems and has evolved into an important universal alternative to the standard circuit …

The performance of the quantum approximate optimization algorithm is evaluated by using
three different measures: the probability of finding the ground state, the energy expectation …