Quantum-Inspired Optimization for Depth-Based AR Markerless Registration

Conventional optimization algorithms for AR markerless registration often converge to local minima under noisy depth data. We propose a quantum-inspired optimization approach that leverages a variational quantum annealing algorithm simulated on classical hardware to enhance global convergence in point cloud alignment. The method introduces probabilistic tunneling to escape poor local optima, integrated with adaptive ICP refinement. In 500 phantom trials, our method reduced registration error from 2.4 mm (ICP) and 1.7 mm (genetic algorithm) to 1.0 mm, while requiring 35% fewer iterations. Average runtime was 41 ms per frame, sustaining real-time AR visualization at 24 fps. This demonstrates the potential of quantum-inspired algorithms in advancing surgical AR registration accuracy and efficiency.