Based on the quantum-assisted genetic algorithm (QAGA) [11] and related approaches we introduce several modifications of QAGA to search for more promising solvers on (at least) graph coloring problems, knapsack problems, Boolean satisfiability problems, and an equal combination of these three. We empirically test the efficiency of these algorithmic changes on a purely classical version of the algorithm (simulated-annealing-assisted genetic algorithm, SAGA) and verify the benefit of selected modifications when using quantum annealing hardware. Our results point towards an inherent benefit of a simpler and more flexible algorithm design.

Archetypes are those extreme values of a data set that can jointly represent all other data points. They often have descriptive meanings and can thus contribute to the understanding of the data. Such archetypes are identified using archetypal analysis and all data points are represented as convex combinations thereof. In this work, archetypal analysis is linked with quantum annealing. For both steps, i.e. the determination of archetypes and the assignment of data points, we derive a QUBO formulation which is solved on D-Wave’s 2000Q Quantum Annealer. For selected data sets, called toy and iris, our quantum annealing-based approach can achieve similar results to the original R-package archetypes.

28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2020)