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

• URL: http://arxiv.org/abs/2505.05212v1
• Date: Thu, 08 May 2025 13:05:07 GMT
• Title: HQC-NBV: A Hybrid Quantum-Classical View Planning Approach
• Authors: Xiaotong Yu, Chang Wen Chen,
• Abstract summary: HQC-NBV is a hybrid quantum-classical framework for view planning.<n>Our approach achieves up to 49.2% higher exploration efficiency across diverse environments.
• Score: 20.480581428768854
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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