{
  "id": "2505.05212",
  "version": "v1",
  "published": "2025-05-08T13:05:07.000Z",
  "updated": "2025-05-08T13:05:07.000Z",
  "title": "HQC-NBV: A Hybrid Quantum-Classical View Planning Approach",
  "authors": [
    "Xiaotong Yu",
    "Chang Wen Chen"
  ],
  "categories": [
    "cs.CV"
  ],
  "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2025-05-08T13:05:07.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "hybrid quantum-classical view planning approach",
      "robotic perception",
      "multi-component cost terms",
      "robotic exploration tasks",
      "higher exploration efficiency"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}