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

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

Quantum simulation has emerged as a valuable arena for demonstrating and understanding
the capabilities of near-term quantum computers,–. Quantum annealing, has been …

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

Quantum annealing (QA) has been proposed as a quantum enhanced optimization heuristic
exploiting tunneling. Here, we demonstrate how finite-range tunneling can provide …

The discovery of Higgs-boson decays in a background of standard-model processes was
assisted by machine learning methods,. The classifiers used to separate signals such as …

The observation of an unequivocal quantum speedup remains an elusive objective for
quantum computing. A more modest goal is to demonstrate a scaling advantage over a class …

We benchmark the quantum processing units of the largest quantum annealers to date, the
5000+ qubit quantum annealer Advantage and its 2000+ qubit predecessor D-Wave 2000Q …

The first generation of quantum computers are on the horizon, fabricated from quantum
hardware platforms that may soon be able to tackle certain tasks that cannot be performed or …

Automated materials design with machine learning is increasingly common in recent years.
Theoretically, it is characterized as black-box optimization in the space of candidate …