On Convergence of Tabu-Enhanced Quantum Annealing Algorithm

Alexander Rumyantsev, Davide Pastorello, Enrico Blanzieri, Valter Cavecchia
The convergence of a recently proposed tabu-enhanced quantum annealing algorithm depends critically on the finiteness of memory (markov property) of the related stochastic process. We discuss the background of quantum annealing and the convergence issues of the tabu-enhanced algorithm. Given the details of the tabu data structure, the so-called tabu matrix, we consider the sequences of solutions that result in a tabu matrix collision (regeneration). As such, convergence of the algorithm is related to the problem of studying the kernel of the matrix, which we investigate and give an example of such a collision as well.