# Quantum-Assisted Automatic Path-Planning for Robotic Quality Inspection in Industry 4.0

arXiv: https://arxiv.org/abs/2507.01462 · HTML: https://mlsanity.com/paper/2507.01462/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 6 references that are in the map.
- First posted on arXiv 2 Jul 2025.

## Builds on (most cited first, 3 of 3)

- [Challenges and Opportunities in Quantum Optimization](https://mlsanity.com/paper/2312.02279/) (2023)
- [D-Wave's Nonlinear-Program Hybrid Solver: Description and Performance Analysis](https://mlsanity.com/paper/2410.07980/) (2024)
- [Navigating Intelligence: A Survey of Google OR-Tools and Machine Learning for Global Path Planning in Autonomous Vehicles](https://mlsanity.com/paper/2503.03338/) (2025)

## Abstract (arXiv)

This work explores the application of hybrid quantum-classical algorithms to optimize robotic inspection trajectories derived from Computer-Aided Design (CAD) models in industrial settings. By modeling the task as a 3D variant of the Traveling Salesman Problem, incorporating incomplete graphs and open-route constraints, this study evaluates the performance of two D-Wave-based solvers against classical methods such as GUROBI and Google OR-Tools. Results across five real-world cases demonstrate competitive solution quality with significantly reduced computation times, highlighting the potential of quantum approaches in automation under Industry 4.0.