Enhancing the efficiency, safety, and speed of large-scale Automated Guided Vehicle (AGV) systems is critical to increasing the productivity of logistics warehouses. Studies using the latest quantum annealers such as "D-Wave Advantage" with over 5000 qubits have shown the potential of quantum annealing (QA) to rapidly optimize AGV routing. However, applying QA to complex and large-scale AGV routing problems is a challenging task due to insufficient consideration of intricate operational conditions, and also due to the insufficient number of qubits in quantum annealers. This paper proposes a refined combinatorial optimization problem that minimizes the total travel time of thousands of AGVs while enhancing safety and efficiency by avoiding collisions. To solve such large-scale optimization problems with thousands of variables, we also propose a novel system architecture containing a Simulated Quantum Annealing (SQA) accelerator using multiple FPGAs. The proposed SQA accelerator is capable of processing problems with over 50,000 variables, which could be a few tens to several hundred times larger than the problems processed on the "D-Wave Advantage". It addresses multiple combinatorial optimization problems across multiple FPGAs concurrently while processing each problem in a high degree of parallelism. We demonstrate the accurate operation of the proposed SQA accelerator using a real-world large-scale AGV system with over 1000 AGVs. According to the experimental results, we observed faster processing speed and better quality results over existing SQA solvers.

In this paper, we propose a novel architecture for a (quasi) optimization problem solver for system designing automation that combines a problem generator and general problem solver. It efficiently generates the number of constraints from a simple description about system requirements, and solves the optimization problem at high speed by utilizing a powerful problem solver. Our challenge is to apply quantum annealing for this purpose. However, we faced the following technical problems caused by the fundamental properties of a quantum annealer: P1) a quantum annealer does not accept a constraint with inequality, which is necessary to describe system design problems, and P2) digit overflow of the coefficient value in the constraints. In this paper, we illustrate the overall architecture of our problem solver and clarify the aforementioned issues. Then, we present solutions to each problem: S1) a conversion method from inequality to equation by the augmented Lagrange method, and S2) a problem conversion method that reduces the number of digits of the coefficient without changing the meaning of the problem. Through experiments with a number of example problems, we evaluated the performance and accuracy of our scheme in comparison with a traditional rigorous problem solver. We show that our scheme outperforms the solver at least in a number of situations, and discuss its future utility.