Application of QUBO solver using black-box optimization to structural design for resonance avoidance
This work addresses a specific bottleneck in structural design for engineers, but it is incremental as it builds on existing BBO methods to extend QUBO solver applications.
The paper tackled the problem of applying QUBO solvers to structural design for resonance avoidance by using black-box optimization methods to overcome the difficulty of transforming original optimization problems into QUBO formulations. It demonstrated that BBO with a factorization machine improved calculation time and success probability in designing a printed circuit board to maximize natural frequency and minimize mounting points.
Quadratic unconstrained binary optimization (QUBO) solvers can be applied to design an optimal structure to avoid resonance. QUBO algorithms that work on a classical or quantum device have succeeded in some industrial applications. However, their applications are still limited due to the difficulty of transforming from the original optimization problem to QUBO. Recently, black-box optimization (BBO) methods have been proposed to tackle this issue using a machine learning technique and a Bayesian treatment for combinatorial optimization. We employed the BBO methods to design a printed circuit board for resonance avoidance. This design problem is formulated to maximize natural frequency and simultaneously minimize the number of mounting points. The natural frequency, which is the bottleneck for the QUBO formulation, is approximated to a quadratic model in the BBO method. We demonstrated that BBO using a factorization machine shows good performance in both the calculation time and the success probability of finding the optimal solution. Our results can open up QUBO solvers' potential for other applications in structural designs.