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Paper Abstract and Keywords
Presentation 2026-04-09 13:20
[Invited Lecture] Topology-Guided Point Sampling for Indoor Propagation Prediction Using Quantum Circuit-Based Learning
Miyuki Hirosed (Kyutech) AP2026-3
Abstract (in Japanese) (See Japanese page) 
(in English) This paper presents a topology-guided sampling and feature construction method for indoor radio propagation prediction using point cloud data. Conventional point-cloud processing methods typically rely on random or geometric down-sampling, which may discard propagation-relevant structures such as boundaries, obstructions, and points within the Fresnel zone. In this study, a topology score is defined for each point based on local geometric properties, including curvature, normal variation, and boundary characteristics, to prioritize points that significantly contribute to radio propagation. Based on the selected points, two types of feature representations are constructed: (1) global features extracted by a PointNet-based classical deep-learning model, and (2) low-dimensional topology-aware descriptors designed for a quantum–classical hybrid model.
Experimental results demonstrate that the proposed topology-guided approach achieves higher prediction accuracy than conventional sampling methods. Furthermore, in the quantum model, the proposed feature representation significantly reduces input dimensionality while maintaining competitive performance. These results indicate that topology-aware point-cloud representation is effective for efficient and accurate radio propagation prediction.
Keyword (in Japanese) (See Japanese page) 
(in English) Indoor propagation prediction / Point Cloud Data / Quantum machine learning / Variational quantum circuits / / / /  
Reference Info. IEICE Tech. Rep., vol. 126, no. 1, AP2026-3, pp. 9-14, April 2026.
Paper # AP2026-3 
Date of Issue 2026-04-02 (AP) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF AP2026-3

Conference Information
Committee AP  
Conference Date 2026-04-09 - 2026-04-10 
Place (in Japanese) (See Japanese page) 
Place (in English) Osaka University Nakanoshima Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Antennas and Propagation, Electoro-magnetic simulation 
Paper Information
Registration To AP 
Conference Code 2026-04-AP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Topology-Guided Point Sampling for Indoor Propagation Prediction Using Quantum Circuit-Based Learning 
Sub Title (in English)  
Keyword(1) Indoor propagation prediction  
Keyword(2) Point Cloud Data  
Keyword(3) Quantum machine learning  
Keyword(4) Variational quantum circuits  
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1st Author's Name Miyuki Hirosed  
1st Author's Affiliation Kyushu Institute of Technology (Kyutech)
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Speaker Author-1 
Date Time 2026-04-09 13:20:00 
Presentation Time 25 minutes 
Registration for AP 
Paper # AP2026-3 
Volume (vol) vol.126 
Number (no) no.1 
Page pp.9-14 
#Pages 6 
Date of Issue 2026-04-02 (AP) 

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