Rotman Lens Design using Factorization Machine and Quantum Annealing

We demonstrate how quantum annealing can support the design of a electromagnetic structure-a Rotman lens. Since the Rotman lens design space spans several billion configurations and electromagnetic simulation of each design takes several hours, exploring even a small fraction of this space is impractical. To address this, we utilize the quantum annealing combined with a factorization machine in an active learning framework to efficiently identify optimal Rotman designs from minimal data. Our results show that, out of a $2^{49}$ design space, an optimal design was found in just 1,684 iterations, achieving performance that surpasses that of a conventional genetic algorithm.