Quantum annealing (QA) can be competitive to classical algorithms in optimizing continuous-variable functions when running on appropriate hardware, show researchers from Tokyo Tech. By comparing the performance of QA running on a D-Wave quantum computer to that of state-of-the-art classical algorithms, they find that a sufficient suppression of thermal noise can enable QA to significantly outperform classical algorithms.
Quantum annealing (QA) is a cutting-edge algorithm that leverages the unique properties of quantum computing to tackle complex combinatorial optimization problems (a class of mathematical problems dealing with discrete-variable functions). Quantum computers use the rules of quantum physics to solve such problems potentially faster than classical computers. In essence, they can explore multiple solutions to a problem simultaneously, giving them a significant speed advantage for certain tasks over classical computers. In particular, QA harnesses the phenomenon of "quantum tunneling," where particles can "tunnel" through energy barriers without the requisite energy to cross over them, to find solutions for combinatorial optimization problems.
Up until now, QA has almost exclusively been used to solve discrete-variable functions (functions that have discrete-valued variables). The potential of QA for optimizing continuous-variable functions has remained largely unexplored.