Quantum annealing is a quantum computing approach widely used for optimization and
probabilistic sampling problems. It is an alternative approach designed due to the limitations …

Quantum annealing has the potential to outperform classical transistor-based computer
technologies in tackling intricate combinatorial optimization problems. However, ongoing …

Quantum annealing has the potential to find low energy solutions of NP-hard problems that
can be expressed as quadratic unconstrained binary optimization problems. However, the …

Leveraging the current generation of quantum devices to solve optimization problems of
practical interest necessitates the development of hybrid quantum-classical (HQC) solution …

Annealers leverage quadratic unconstrained binary optimization (QUBO) formulas to
address combinatorial optimization problems (COPs) and have shown potential to …

Quantum (-inspired) annealers show promise in solving combinatorial optimisation
problems in practice. There has been extensive researches demonstrating the utility of D …

The expectations arising from the latest achievements in the quantum computing field are
causing that researchers coming from classical artificial intelligence to be fascinated by this …

Quadratic unconstrained binary optimization (QUBO) formulas of quantum annealing (QA)
algorithms are classified into four categories. QA algorithms using different QUBO formulas …

This paper describes the components and configurations available in a new quantum-
assisted machine learning (QAML) framework. QAML is an open source package that …

Current quantum computers can only solve optimization problems of a very limited size. For
larger problems, decomposition methods are required in which the original problem is …