Quantum Neural Regressors in Localization Tasks for Wireless Communications Networks

Fernando M. de Paula Net

¹Centro de Inform´atica, Universidade Federal de Pernambuco, Recife, Pernambuco, Brazil

Email: [email protected]

²Centro de Inform´atica, Universidade Federal de Pernambuco, Recife, Pernambuco, Brazil

Email: [email protected]

³Centro de Inform´atica, Universidade Federal de Pernambuco, Recife, Pernambuco, Brazil

Email: [email protected]

*Corresponding Author: Fernando M. de Paula Neto, Email: [email protected]

In this work, we applied two quantum neural circuit (QNC) models as regressors for the average localization error prediction problem in a wireless sensor network and for mobile phone position estimation using a wireless local area network (WLAN)- fingerprinting-based positioning strategy. We trained the QNCs using the variational circuit strategy with three different types of optimizers. The results were compared with six classical regressors widely used in the literature: linear regressor (LR), support vector regressor (SVR), multilayer perceptron regressor (MLPR), decision tree regressor (DTR), K-nearest neighbors regressor (KNNR), and stochastic gradient descent regressor (SGDR). Different configurations of the SVR and MLPR models were tested. The results showed that quantum circuit optimization was promising for these localization tasks and that a QNC model (i.e., a single quantum neuron) was statistically equivalent to, or even superior to, the six classical regressor models in many cases (according to the Wilcoxon test). The results obtained in this work are promising for quantum computing, particularly as small-scale quantum systems become increasingly common.

Quantum Neural Networks, Quantum Machine Learning, Wireless Sensor Networks, Indoor Localization

Vinicius R. de Albuquerque, Fernando M. de Paula Neto, Daniel C. Cunha (2026). Quantum Neural Regressors in Localization Tasks for Wireless Communications Networks. Journal of Artificial Intelligence and Systems, 8, 1–17. https://doi.org/10.33969/AIS.2026.080101.

[1] P. W. Shor, Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer, SIAM Journal on Computing 26 (5) (1997) 1484–1509.

[2] J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, S. Lloyd, Quantum machine learning, Nature 549 (7671) (2017) 195–202.

[3] H. Zhao, A. Zlokapa, H. Neven, R. Babbush, J. Preskill, J. R. McClean, H.-Y. Huang, Exponential quantum advantage in processing massive classical data, arXiv preprint arXiv:2604.07639 (2026).

[4] R. Rahman, D. C. Nguyen, Quantum convolutional neural networks: A survey on architectures, applications, and future directions, IEEE Transactions on Neural Networks and Learning Systems (2026).

[5] A. Verdone, F. Succetti, A. Ceschini, A. Rosato, A. Fioravanti, M. Panella, A hybrid quantum-neural network for heart disease classification, Biomedical Signal Processing and Control 113 (2026) 109185.

[6] S. Mangini, F. Tacchino, D. Gerace, C. Macchiavello, D. Bajoni, Quantum computing model of an artificial neuron with continuously valued input data, Machine Learning: Science and Technology 1 (4) (2020) 045008.

[7] J. H. A. de Carvalho, F. M. de Paula Neto, Parametrized constant-depth quantum neuron, IEEE Trans. Neural Netw. Learn. Syst. 35 (11) (2024) 15932–15943. doi: 10.1109/TNNLS.2023.3290535.

[8] A. Abbas, D. Sutter, C. Zoufal, A. Lucchi, A. Figalli, S. Woerner, The power of quantum neural networks, Nature Computational Science 1 (2021) 403–409.

[9] J. J¨ager, R. V. Krems, Universal expressiveness of variational quantum classifiers and quantum kernels for support vector machines, Nature Communications 14 (1) (2023) 576.

[10] M. Schuld, R. Sweke, J. J. Meyer, Effect of data encoding on the expressive power of variational quantum-machine-learning models, Physical Review A 103 (3) (2021) 032430.

[11] S. Chen, S. Yoo, Y. Fang, et al., Universal approximation theorem and error bounds for quantum neural networks and quantum reservoirs, IEEE Transactions on Neural Networks and Learning SystemsEarly access / preprint versions may exist (2022).

[12] F. M. Neto, Data complexity measures for quantum circuits architecture recommendation, arXiv preprint arXiv:2502.15129 (2025).

[13] F. M. Neto, L. d. R. Silva, P. S. Neto, F. F. Fanchini, Regression of functions by quantum neural networks circuits, arXiv preprint arXiv:2512.19978 (2025).

[14] J. R. McClean, S. Boixo, V. N. Smelyanskiy, R. Babbush, H. Neven, Barren plateaus in quantum neural network training landscapes, Nature communications 9 (1) (2018) 4812.

[15] R. D. Timoteo, D. C. Cunha, A scalable fingerprint-based angle-of-arrival machine learning approach for cellular mobile radio localization, Computer Communications 157 (2020) 92–101.

[16] H. Ge, Z. Sun, Y. Chiba, N. Koshizuka, Accurate indoor location awareness based on machine learning of environmental sensing data, Computers & Electrical Engineering 98 (2022) 107676.

[17] S. Kagi, B. S. Mathapati, Localization in wireless sensor network using machine learning optimal trained deep neural network by parametric analysis, Measurement: Sensors 24 (2022) 100427.

[18] H. Attar, W. Saber Ismail, M. Hafez, S. Bahaa, M. A. Deif, M. Khosravi, H. Youssry, Machine learning in indoor localization prediction using received signal strength indicator and Wi-Fi network, Int. J. Intell. Networks 6 (2025) 233–243.

[19] A. Gopakumar, L. Jacob, Localization in wireless sensor networks using particle swarm optimization, in: 2008 IET International Conference on Wireless, Mobile and Multimedia Networks, IET, 2008, pp. 227–230.

[20] S. Goyal, M. S. Patterh, Wireless sensor network localization based on Cuckoo search algorithm, Wireless Personal Communications 79 (1) (2014) 223–234.

[21] J. Cheng, L. Xia, An effective cuckoo search algorithm for node localization in wireless sensor network, Sensors 16 (9) (2016) 1390.

[22] A. Singh, V. Kotiyal, S. Sharma, J. Nagar, C.-C. Lee, A machine learning approach to predict the average localization error with applications to wireless sensor networks, IEEE Access 8 (2020) 208253–208263.

[23] F. Meneses, A. Moreira, A. Costa, M. J. Nicolau, Radio maps for fingerprinting in indoor positioning, in: J. Conesa, A. P´erez-Navarro, J. Torres-Sospedra, R. Montoliu (Eds.), Geographical and Fingerprinting Data to Create Systems for Indoor Positioning and Indoor/Outdoor Navigation, Academic Press, 2019, pp. 69–95. doi:https: //doi.org/10.1016/B978-0-12-813189-3.00004-6.

[24] Y. Wang, R. Sun, Q. Cheng, W. Y. Ochieng, Measurement quality control aided multisensor system for improved vehicle navigation in urban areas, IEEE Trans. Ind. Electron. 71 (6) (2024) 6407–6417. doi:10.1109/TIE.2023.3288188.

[25] T. Koike-Akino, P. Wang, Y. Wang, AutoQML: Automated quantum machine learning for Wi-Fi integrated sensing and communications (2022). arXiv:2205.09115. URL https://arxiv.org/abs/2205.09115

[26] S. Ahmed, M. Youssef, Device-independent quantum fingerprinting for large scale localization, in: 2022 20th Mediterranean Communication and Computer Networking Conference (MedComNet), IEEE, 2022, pp. 208–215.

[27] S. Ahmed, M. Youssef, Quantum fingerprinting for heterogeneous devices localization, Computer Communications 204 (2023) 43–51.

[28] E. Guler, M. T. Kakız, T. C¸ avdar, F. B. G¨unay, B. S¸anal, A quantum machine learning approach for detecting user locations, Journal of Millimeterwave Communication, Optimization and Modelling 3 (1) (2023) 1–8.

[29] M. Jeong, H. Park, S. W. Choi, S. Kim, et al., Fingerprinting-based indoor localization with hybrid quantum-deep neural network, IEEE Access 11 (2023) 142276–142291.

[30] S. J. Nawaz, S. K. Sharma, S. Wyne, M. N. Patwary, M. Asaduzzaman, Quantum machine learning for 6g communication networks: State-of-the-art and vision for the future, IEEE Access 7 (2019) 46317–46350.

[31] M. A. Nielsen, I. L. Chuang, Quantum Computation and Quantum Information, Cambridge University Press, 2000.

[32] S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall PTR, 1994.

[33] M. Benedetti, E. Lloyd, S. Sack, M. Fiorentini, Parameterized quantum circuits as machine learning models, Quantum Science and Technology 4 (4) (2019) 043001.

[34] Y. Huang, H. Lei, X. Li, An empirical study of optimizers for quantum machine learning, in: 2020 IEEE 6th International Conference on Computer and Communications (ICCC), IEEE, 2020, pp. 1560–1566.

[35] M. Cerezo, A. Arrasmith, R. Babbush, S. C. Benjamin, S. Endo, K. Fujii, J. R. McClean,

K. Mitarai, X. Yuan, L. Cincio, et al., Variational quantum algorithms, Nature Reviews Physics 3 (9) (2021) 625–644.

[36] V. Bergholm, J. Izaac, M. Schuld, C. Gogolin, S. Ahmed, V. Ajith, M. S. Alam, G. Alonso-Linaje, B. AkashNarayanan, A. Asadi, et al., Pennylane: Automatic differentiation of hybrid quantum-classical computations, arXiv preprint arXiv:1811.04968 (2018).

[37] J. Torres-Sospedra, R. Montoliu, A. Mart´ınez-Us´o, J. P. Avariento, T. J. Arnau,

M. Benedito-Bordonau, J. Huerta, UJIIndoorLoc: A new multi-building and multi-floor database for WLAN fingerprint-based indoor localization problems, in: 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN), IEEE, 2014, pp. 261–270.

[38] R. Bro, A. K. Smilde, Principal component analysis, Analytical Methods 6 (9) (2014) 2812–2831.

[39] J. Preskill, Quantum computing in the nisq era and beyond, Quantum 2 (2018) 79.