Disturbance Insertion for Improving Quantum and GPU Annealer Performance
Kuan-Chen Chou, Jehn‐Ruey Jiang · 2024
This paper investigates the impact of introducing disturbances into quadratic unconstrained binary optimization (QUBO) formulas to enhance the performance of both the quantum annealer (QA) and the GPU annealer (GPUA). We propose a disturbance insertion method to insert into QUBO formula coefficients some disturbances that are parameterized by the insertion ratio, Gaussian distribution mean, and standard deviation. By applying the proposed method to a QA (viz., the D-Wave Advantage annealer) and a GPUA (viz., the Compal Quantix solver) for solving two well-known combinatorial optimization problems, the traveling salesperson problem and the 0/1 knapsack problem, we identify appropriate disturbance parameters that facilitate the annealing process for better solving the two problems.