Vol. 3 No. 1 (2023): Journal of Millimeterwave Communication, Optimization and Modelling
Articles

A Quantum Machine Learning Approach for Detecting User Locations

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  • Erkan Guler,
  • Muhammet Talha Kakız,
  • Tuğrul Çavdar,
  • Faruk Baturalp Günay,
  • Burcu Şanal
Erkan Guler
Giresun University
JOMCOM 3(1) Cover

Published 31.07.2023

Copyright (c) 2023 Erkan Guler; Muhammet Talha Kakız, Tuğrul Çavdar, Faruk Baturalp Günay, Burcu Şanal

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

Quantum Machine Learning methods are becoming a key component for various types of tasks making predictions or decisions based on datasets. Recent efforts and researches on quantum computing point out the significance of quantum speedup advantage, especially for learning processes that require enormous amount of computational resources. Advances in both quantum hardware design and hybrid quantum-classical software frameworks accommodate a paradigm shift from classical to quantum. In consideration with this quantum leap notion, we investigate the capability of variational quantum algorithms (VQA) on a real world problem of user localization dealing with the binary classification task. This paper introduces a VQA with four variants that differ in the number of layers related to the variational quantum circuit (VQC) part of the VQA. The samples from a publicly available user localization dataset are first preprocessed through padding, scaling and normalization. Next, they are mapped into three qubit quantum states using amplitude encoding as a data embedding scheme. Unitary transformation of the mapped quantum data in the VQC is followed by a measurement in computational basis to produce predictions for the labels. The error between true and predicted labels is computed in a classical manner and a cost function minimization process is executed with the aid of gradient descent algorithm. The updated training parameters from the optimization stage are fed into the VQC and this process is repeated until the learnable parameters converge. The simulation results demonstrate that the designed VQA for binary classification achieves an accuracy value of 99% in the training phase. Moreover, the ratio of predicted labels to true labels approaches to 93% during the validation of actual user locations based on the signal strength received from the routers that are positioned at different places in a facility.

Keywords—quantum machine learning, user localization, variational quantum algorithm, variational quantum circuit, amplitude encoding

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References

  1. Michael A. Nielsen and Isaac Chuang, Quantum computation and quantum information. Cambridge, UK: Cambridge University Press, 2002.
  2. Peter W. Shor, “Algorithms for quantum computation: discrete logarithms and factoring,” in Proc. 35th annual symposium on foundations of computer science, 1994, pp. 124-134.
  3. Lov K. Grover, “A fast quantum mechanical algorithm for database search,” in Proc. the twenty-eighth annual ACM symposium on Theory of computing, 1996, pp. 212-219.
  4. Seth Lloyd, "Universal quantum simulators," Science, vol. 273, no. 5278, pp. 1073-1078, Aug. 23, 1996.
  5. Robin Harper, Steven T. Flammia, and Joel J. Wallman, “Efficient learning of quantum noise,” Nature Physics, vol. 16, no. 12, pp. 1184-1188, Dec. 2020.
  6. John Preskill, “Quantum computing in the NISQ era and beyond,” Quantum, vol. 2, no. 79, pp. 1-20, Jul. 30, 2018.
  7. Sukhpal Singh Gill et al., “Quantum computing: A taxonomy, systematic review and future directions,” Software: Practice and Experience, vol. 52, no. 1, pp. 66-114, Oct. 7, 2022.
  8. N. Schetakis, D. Aghamalyan, P. Griffin, and M. Boguslavsky, “Review of some existing QML frameworks and novel hybrid classical–quantum neural networks realising binary classification for the noisy datasets,” Scientific Reports, vol. 12, no. 1, pp. 1-12, Jul. 13, 2022.
  9. Maria Schuld and Francesco Petruccione, Machine learning with quantum computers, Berlin, Germany: Springer, 2021.
  10. Tariq M. Khan and Antonio Robles-Kelly, “Machine learning: quantum vs classical,” IEEE Access, vol. 8, pp. 219275-219294, Dec. 1, 2020.
  11. L. Alchieri, D. Badalotti, P. Bonardi, and S. Bianco, “An introduction to quantum machine learning: from quantum logic to quantum deep learning,” Quantum Machine Intelligence, vol. 3, pp. 1-30, Nov. 15, 2021.
  12. S. Y. C. Chen and S. Yoo, “Federated quantum machine learning,” Entropy, vol. 23, no. 4, pp. 1-15, Apr. 13, 2021.
  13. Y. Du, T. Huang, S. You, M. H. Hsieh, and D. Tao, “Quantum circuit architecture search: error mitigation and trainability enhancement for variational quantum solvers,” 2020, arXiv:2010.10217.
  14. T. Hubregtsen, J. Pichlmeier, P. Stecher, and K. Bertels, “Evaluation of parameterized quantum circuits: on the relation between classification accuracy, expressibility, and entangling capability,” Quantum Machine Intelligence, vol. 3, pp. 1-19, Mar. 11, 2021.
  15. Samuel Yen-Chi Chen et al., “Variational quantum circuits for deep reinforcement learning,” IEEE Access, vol. 8, pp. 141007-141024, Jul. 20, 2020.
  16. A. Al-Habashna, G. Wainer, and Moayad Aloqaily, “Machine learning-based indoor localization and occupancy estimation using 5G ultra-dense networks,” Simulation Modelling Practice and Theory, vol. 118, pp. 1-17, 2022.
  17. P. S. Varma and Veena Anand, “Fault-Tolerant indoor localization based on speed conscious recurrent neural network using Kullback–Leibler divergence,” Peer-to-Peer Networking and Applications, vol. 15, no. 3, pp. 1370-1384, Feb. 25, 2022.
  18. K. Ngamakeur, S. Yongchareon, J. Yu, and S. Islam, “Passive infrared sensor dataset and deep learning models for device-free indoor localization and tracking,” Pervasive and Mobile Computing, vol. 88, pp. 1-16, 2023.
  19. J. Xue, J. Liu, M. Sheng, Y. Shi, and J. Li, “A WiFi fingerprint based high-adaptability indoor localization via machine learning,” China Communications, vol. 17, no. 7, pp. 247-259, Jul. 24, 2020.