Optimization of Sensor-Placement on Vehicles using Quantum-Classical Hybrid Methods

TL;DR

This paper explores quantum-classical hybrid methods for optimizing sensor placement on vehicles, demonstrating their potential through simulation studies within current quantum computing constraints.

Contribution

It introduces two quantum-enhanced formulations for sensor placement optimization and adapts them for current quantum hardware and simulators.

Findings

Quantum-inspired formulations work effectively in simulations

Hybrid methods show promise for complex vehicle sensor placement

Results validate the potential of quantum approaches in automotive safety

Abstract

Placement of sensors on vehicles for safety and autonomous capability is a complex optimization problem when considered in the full-blown form, with different constraints. Considering that Quantum Computers are expected to be able to solve certain optimization problems more "easily" in the future, the problem was posted as part of the BMW Quantum Computing Challenge 2021. In this paper, we have presented two formulations for quantum-enhanced solutions in a systematic manner. In the process, necessary simplifications are invoked to accommodate the current capabilities of Quantum Simulators and Hardware. The presented results and observations from elaborate simulation studies demonstrate the correct functionality and usefulness of the proposals.