# HQC-NBV: A Hybrid Quantum-Classical View Planning Approach

arXiv: https://arxiv.org/abs/2505.05212 · HTML: https://mlsanity.com/paper/2505.05212/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 8 May 2025.

## Builds on (most cited first, 13 of 13)

- [NeU-NBV: Next Best View Planning Using Uncertainty Estimation in Image-Based Neural Rendering](https://mlsanity.com/paper/2303.01284/) (2023)
- [An Efficient Sampling-based Method for Online Informative Path Planning in Unknown Environments](https://mlsanity.com/paper/1909.09548/) (2019)
- [Q-Match: Iterative Shape Matching via Quantum Annealing](https://mlsanity.com/paper/2105.02878/) (2021)
- [Adiabatic Quantum Computing for Multi Object Tracking](https://mlsanity.com/paper/2202.08837/) (2022)
- [Adiabatic Quantum Graph Matching with Permutation Matrix Constraints](https://mlsanity.com/paper/2107.04032/) (2021)
- [Quantum Motion Segmentation](https://mlsanity.com/paper/2203.13185/) (2022)
- [Autonomous UAV Exploration of Dynamic Environments via Incremental Sampling and Probabilistic Roadmap](https://mlsanity.com/paper/2010.07429/) (2020)
- [A Multi-Resolution Frontier-Based Planner for Autonomous 3D Exploration](https://mlsanity.com/paper/2011.02182/) (2020)
- [Quantum Multi-Model Fitting](https://mlsanity.com/paper/2303.15444/) (2023)
- [A Shadowcasting-Based Next-Best-View Planner for Autonomous 3D Exploration](https://mlsanity.com/paper/2109.09323/) (2021)
- [NBV-SC: Next Best View Planning based on Shape Completion for Fruit Mapping and Reconstruction](https://mlsanity.com/paper/2209.15376/) (2022)
- [Graph-based View Motion Planning for Fruit Detection](https://mlsanity.com/paper/2303.03048/) (2023)
- [Rapidly-Exploring Random Graph Next-Best View Exploration for Ground Vehicles](https://mlsanity.com/paper/2108.01012/) (2021)

## Abstract (arXiv)

Efficient view planning is a fundamental challenge in computer vision and robotic perception, critical for tasks ranging from search and rescue operations to autonomous navigation. While classical approaches, including sampling-based and deterministic methods, have shown promise in planning camera viewpoints for scene exploration, they often struggle with computational scalability and solution optimality in complex settings. This study introduces HQC-NBV, a hybrid quantum-classical framework for view planning that leverages quantum properties to efficiently explore the parameter space while maintaining robustness and scalability. We propose a specific Hamiltonian formulation with multi-component cost terms and a parameter-centric variational ansatz with bidirectional alternating entanglement patterns that capture the hierarchical dependencies between viewpoint parameters. Comprehensive experiments demonstrate that quantum-specific components provide measurable performance advantages. Compared to the classical methods, our approach achieves up to 49.2% higher exploration efficiency across diverse environments. Our analysis of entanglement architecture and coherence-preserving terms provides insights into the mechanisms of quantum advantage in robotic exploration tasks. This work represents a significant advancement in integrating quantum computing into robotic perception systems, offering a paradigm-shifting solution for various robot vision tasks.