We compare the performance of four quantum annealers, the D-Wave Two, 2X, 2000Q, and
Advantage in solving an identical ensemble of a parametrized family of scheduling …

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 …

We report on a case study in programming an early quantum annealer to attack optimization
problems related to operational planning. While a number of studies have looked at the …

An effective approach to solving complex problems is to decompose them and integrate
dedicated solvers for those subproblems. We introduce a hybrid decomposition that …

In the evaluation of quantum annealers, metrics based on ground state success rates have
two major drawbacks. First, evaluation requires computation time for both quantum and …

Quantum computers have provided a promising tool for tackling NP hard problems.
However, most of the existing work on quantum annealers assumes exclusive access to all …

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

To solve an optimization problem using a commercial quantum annealer, one has to
represent the problem of interest as an Ising or a quadratic unconstrained binary …

Abstract Job Shop Scheduling is a combinatorial optimization problem of particular
importance for production environments where the goal is to complete a production task in …

Recently, many researchers are investigating quantum annealing as a solver for real-world
combinatorial optimization problems. However, due to the format of problems that quantum …