Ising machines are hardware solvers that aim to find the absolute or approximate ground
states of the Ising model. The Ising model is of fundamental computational interest because …

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

Experiments on disordered alloys,–suggest that spin glasses can be brought into low-
energy states faster by annealing quantum fluctuations than by conventional thermal …

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

The Fujitsu Digital Annealer is designed to solve fully connected quadratic unconstrained
binary optimization (QUBO) problems. It is implemented on application-specific CMOS …

This paper presents an overview of the topology of D-Wave's next-generation quantum
processors. It provides examples of minor embeddings and discusses performance of …

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

Combinatorial optimization is anticipated to be one of the primary use cases for quantum
computation in the coming years. The Quantum Approximate Optimization Algorithm and …

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 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 …