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

TL;DR

HQC-NBV introduces a hybrid quantum-classical framework for view planning in robotics, leveraging quantum properties to improve exploration efficiency and scalability over classical methods.

Contribution

The paper presents a novel quantum-inspired view planning approach that enhances exploration efficiency and scalability in robotic perception tasks.

Findings

Achieves up to 49.2% higher exploration efficiency

Demonstrates measurable performance advantages of quantum components

Provides insights into quantum advantage mechanisms in robotic exploration

Abstract

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.