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Paper Abstract and Keywords
Presentation 2025-12-11 13:30
Quantum Circuit-Based Learning for Indoor Propagation Estimation Using Point Cloud Data
Miyuki Hirose (Kyutech) AP2025-172
Abstract (in Japanese) (See Japanese page) 
(in English) This paper introduces a topology-guided framework for efficient indoor propagation prediction using point cloud data. Two complementary approaches are developed: (1) a classical deep-learning model based on PointNet combined with topology-aware sampling, and (2) a quantum–classical hybrid model employing correlation-driven feature extraction with Variational Quantum Circuits (VQC). The classical model enhances propagation-relevant feature selection by exploiting geometric and topological cues—including reflectors, obstructions, and Fresnel zones—achieving a root mean square error (RMSE) of 1.66 dB across ten diverse indoor environments. The hybrid model encodes a topologically compressed representation into eight quantum features, maintaining comparable accuracy (RMSE = 2.13 dB) while reducing memory usage by 90% and enabling faster inference. These findings demonstrate that topology-guided geometric sampling effectively links spatial structure with radio propagation behavior, and that quantum feature encoding provides an efficient and scalable solution for next-generation wireless systems.
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. 125, no. 284, AP2025-172, pp. 1-6, Dec. 2025.
Paper # AP2025-172 
Date of Issue 2025-12-04 (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 AP2025-172

Conference Information
Committee AP  
Conference Date 2025-12-11 - 2025-12-11 
Place (in Japanese) (See Japanese page) 
Place (in English) Kikai-Shinko-Kaikan Bldg. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Antennas and Propagation 
Paper Information
Registration To AP 
Conference Code 2025-12-AP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Quantum Circuit-Based Learning for Indoor Propagation Estimation Using Point Cloud Data 
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 Hirose  
1st Author's Affiliation Kyushu Institute of Technology (Kyutech)
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Speaker Author-1 
Date Time 2025-12-11 13:30:00 
Presentation Time 25 minutes 
Registration for AP 
Paper # AP2025-172 
Volume (vol) vol.125 
Number (no) no.284 
Page pp.1-6 
#Pages 6 
Date of Issue 2025-12-04 (AP) 

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