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Applying Ising Machines to Multi-objective QUBOs
In this work, we extend the adaptive method based on averages in two ways: (i)~we extend the adaptive method of deriving scalarisation weights for problems with two or more objectives, and (ii)~we use an alternative measure of distance to improve performance.
A Study of Scalarisation Techniques for Multi-Objective QUBO Solving
These solvers are then applied to QUBO formulations of combinatorial optimisation problems.
Penalty Weights in QUBO Formulations: Permutation Problems
Many of these solver can only optimise problems that are in binary and quadratic form.
Comparing the Digital Annealer with Classical Evolutionary Algorithm
The question addressed in this paper is whether quantum or quantum-inspired solvers can optimise QUBO transformations of combinatorial optimisation problems faster than classical evolutionary algorithms applied to the same problems in their natural representations.
Multi-objective QUBO Solver: Bi-objective Quadratic Assignment
We present the first attempt to extend the algorithm supporting a commercial QUBO solver as a multi-objective solver that is not based on scalarisation.
