Quantum-Inspired Diffusion Models for Multi-Robot Coordination

Liang et al. (2025) showed diffusion models excel in MRMP but struggle with constraint satisfaction. This research introduces quantum-constrained diffusion, embedding quantum annealing concepts into the sampling process to handle collision avoidance and kinematics simultaneously. By mapping robot trajectories to qubit states and using quantum tunneling to escape local minima, the planner avoids the inefficiency of classical constraint-handling (e.g., SMD’s optimization loops). Compared to SMD, this approach could reduce planning time exponentially in dense environments (similar to Yang & Shimosaka’s templates but for learning-based methods). Early prototypes could use simulated quantum annealers (e.g., D-Wave) to validate feasibility.

References:

1. Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models. Jinhao Liang, Jacob Christopher, Sven Koenig, Ferdinando Fioretto (2025). arXiv.org.
2. Efficient and Asymptotically Optimal Vehicle Motion Planning With Stochastic Template-Based RRT*. Shaoyu Yang, Masamichi Shimosaka (2025). IEEE Access.