Optimal box contraction for solving linear systems via simulated and quantum annealing

Abstract

Solving linear systems of equations is an important problem in engineering. Many quantum algorithms, such as the Harrow–Hassidim–Lloyd algorithm and the box algorithm, have been proposed for solving such systems. The focus of this article is on improving the efficiency of the box algorithm. The basic principle behind this algorithm is to transform the linear system into a series of quadratic unconstrained binary optimization (QUBO) problems, which are then solved on annealing machines. The computational efficiency of the box algorithm is entirely determined by the number of iterations, which, in turn, depends on the box contraction ratio, typically set to 0.5. Here, it is shown through theoretical analysis that a contraction ratio of 0.5 is sub-optimal and that a computational speed-up can be achieved with a contraction ratio of 0.2. This is confirmed through numerical experiments where a computational speed-up between $ 20 \% $ 20% to $ 60 \% $ 60% is observed when the optimal contraction ratio is used.