Quantum Annealing Hyperparameter Analysis for Optimal Sensor Placement in Production Environments

TL;DR

This paper investigates the application of quantum annealing to optimize sensor placement in automotive manufacturing, demonstrating its potential to solve large-scale, real-world problems more efficiently than classical heuristics.

Contribution

It introduces a quantum annealing approach with hyperparameter tuning for sensor placement optimization, showing scalability and practical relevance in industrial scenarios.

Findings

Quantum annealing can solve real-world sensor placement problems.

Hyperparameter optimization improves solution quality.

Decomposition techniques enable larger problem instances.

Abstract

To increase efficiency in automotive manufacturing, newly produced vehicles can move autonomously from the production line to the distribution area. This requires an optimal placement of sensors to ensure full coverage while minimizing the number of sensors used. The underlying optimization problem poses a computational challenge due to its large-scale nature. Currently, classical solvers rely on heuristics, often yielding non-optimal solutions for large instances, resulting in suboptimal sensor distributions and increased operational costs. We explore quantum computing methods that may outperform classical heuristics in the future. We implemented quantum annealing with D-Wave, transforming the problem into a quadratic unconstrained binary optimization formulation with one-hot and binary encoding. Hyperparameters like the penalty terms and the annealing time are optimized and the results are compared with default parameter settings. Our results demonstrate that quantum annealing is capable of solving instances derived from real-world scenarios. Through the use of decomposition techniques, we are able to scale the problem size further, bringing it closer to practical, industrial applicability. Through this work, we provide key insights into the importance of quantum annealing parametrization, demonstrating how quantum computing could contribute to cost-efficient, large-scale optimization problems once the hardware matures.