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 …

Quantum annealing (QA) is a heuristic quantum optimization algorithm that can be used to
solve combinatorial optimization problems. In recent years, advances in quantum …

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

Hybrid quantum-classical algorithms are central to much of the current research in quantum
computing, particularly when considering the noisy intermediate-scale quantum (NISQ) era …

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 …

We investigate a hybrid quantum-classical solution method to the mean-variance portfolio
optimization problems. Starting from real financial data statistics and following the principles …

The Capacitated Vehicle Routing Problem (CVRP) is an NP-optimization problem (NPO)
that has been of great interest for decades for both, science and industry. The CVRP is a …

In this paper I propose a new method of encoding discrete variables into Ising model qubits
for quantum optimisation. The new method is based on the physics of domain walls in one …

Quantum annealing is a heuristic algorithm that solves combinatorial optimization problems,
and D-Wave Systems Inc. has developed hardware implementation of this algorithm …