Synergistic Integration of Quantum and Classical Machine Learning Models for High-Fidelity Asteroid Hazard Detection
DOI:
https://doi.org/10.4108/eetiot.8170Keywords:
Quantum machine learning, Quantum computing, Potentially Hazardous Asteroids (PHAs), Quantum Convolutional Neural Networks (QCNN), Space Hazard Assessment, quantum algorithm optimization, Recursive Feature Elimination(RFE)Abstract
This research investigates the use of quantum machine learning (QML) to classify asteroids into non-hazardous and hazardous groups, which yields successful results in detecting the hazard. In addition to the complexity involved in analyzing orbits and physical objects, QML performs better than traditional machine learning in modeling the relationship between data. This method involves data-intensive preprocessing steps, such as feature selection by removing unnecessary features and correlation analysis to find predictors. Quantum circuits are used for specification and classification, and the standard evaluation is based on accuracy, recall, F1 score, and precision. A strong and weak method is provided by cross-validation and hyperparameter tuning. The best classical model here is the decision tree, which is a good model for high-resolution and low-budget social benefit with 0.883 accuracy, 0.955 recovery rate, 0.981 F1 score, and 0.883 sensitivity. However, the quantum model has made great leaps. AMSGRAD QCNN (Adaptive Moment Estimation with Gradient Thresholding Quantum Convolutional Neural Networks) achieves a non-uniform accuracy of 0.997, which is 13% higher than the decision tree with 0.984 accuracy, 0.955 recovery rate, and 0.981 F1 score. The accuracy of SPSA QCNN(Simultaneous Perturbation Stochastic Approximation) is 0.993, the recall is 0.974, and the F1 score is 0.977. This improvement shows the excellent ability of the quantum model to better resolve correlation data and, more importantly, to reduce false negatives in detecting stars. These results demonstrate the ability of quantum computing to analyze complex data and provide the best results. Our future work will focus on identifying and repairing quantum circuits, as well as exploring hybrid quantum classical models to improve model accuracy and interpretation. The findings open the door to new ways to predict the simplest solutions and the most powerful and accurate way to date of estimating the danger zone.
Downloads
References
[1] Scheurer, M., Anselmetti, G. L. R., Oumarou, O., Gogolin, C., & Rubin, N. C. (2024). Tailored and externally corrected coupled cluster with quantum inputs. arXiv preprint, arXiv:2312.08110. https://arxiv.org/abs/2312.08110
[2] Rebentrost, P., Mohseni, M., & Lloyd, S. (2014). Quantum support vector machine for big data classification. Physical Review Letters, 113(13), 130503S.
[3] S. P. M. S. P. M. D. J. a. R. L. V. Rodriguez-Fernandez, "Towards a Machine Learning-Based Approach to Predict Space Object Density Distributions," 2024.
[4] P. P. Bhagwakar, C. S. Thaker, and [Third Author's Initials and Last Name], "A review of quantum algorithms for prediction of hazardous asteroids," Computing and Artificial Intelligence, vol. 2, no. 1, pp. 1-10, May 2024.
[5] M. S. a. N. K. E. Zahedinejad, "Quantum variational algorithms for the Kepler problem," 2020.
[6] M. Weigand, "Quantum algorithms for space situational awareness," arXiv preprint arXiv:2109.05728, Sep. 2021. [Online]. Available: https://arxiv.org/abs/2109.05728.
[7] T. G. Dietterich, "Overfitting and under computing in machine learning," Computing Surveys, vol. 27, no. 3, pp. 326–327, 1995. [Online]. Available: https://dl.acm.org/doi/10.1145/212094.212114.T. Dietterich, "Overfitting and under computing in machine learning," 1995.
[8] M.-M. Wang and X.-Y. Zhang, "Quantum Bayes classifiers and their application in image classification," arXiv preprint arXiv:2401.09928, Jan. 2024. [Online]. Available: https://arxiv.org/abs/2401.09928.
[9] G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, "A simple way to prevent neural networks from overfitting," Journal of Machine Learning Research, vol. 15, no. 56, pp. 1929–1958, 2014. [Online]. Available:https://www.jmlr.org/papers/volume15/hinton14a/hinton14a.pdf.
[10] T. Van Voorhis, " An overview of developments in the Q-Chem 5 package.," The Journal of Chemical Physics, 155., 2021.
[11] G.-L. R. Anselmetti, O. Ou, and M. Scheurer, "Tailored and externally corrected coupled cluster with quantum inputs," arXiv preprint arXiv:2401.02313, Jan. 2024. [Online]. Available:
[12] P. H. N. Simões, M. R. D. Monteiro, and D. M. Batista, "Experimental Evaluation of Quantum Machine Learning Algorithms," in Proceedings of the International Conference on Quantum Computing and Machine Learning (QCML), Lisbon, Portugal, Apr. 2023, pp. 45–56.
[13] M. S. Hossain and M. A. Zabed, "Machine Learning Approaches for Classification and Diameter Prediction of Asteroids," in Advances in Space Research, vol. 12, no. 1, pp. 123–145, Jan. 2023.
[14] M. Biswal, "A Short Review on Machine Learning in Space Science and Exploration," UG Researcher, Department of Computer Science and Engineering, University College of Engineering Villupuram, India, 2024.
[15] T. Van Voorhis, "Software for the frontiers of quantum chemistry: An overview," The Journal of Chemical Physics, vol. 155, pp. 1–10, 2021.
[16] Y.-J. Lu, A. Singh, and F. P. R. Dilip, "Data compression for quantum machine learning," Physical Review Research, vol. 4, no. 4, pp. 1–12, 2022
[17] S. S. Gill, O. Cetinkaya, S. Marrone, D. Claudino, D. Haunschild, L. Schlote, H. Wu, C. Ottaviani, X. Liu, S. P. Machupalli, K. Kaur, P. Arora, J. Liu, A. Farouk, H. H. Song, S. Uhlig, and K. Ramamohanarao, "Quantum Computing: Vision and Challenges," arXiv preprint arXiv:2403.02240, Mar. 2024.
[18] D. Jewitt, A. Moro-Martín, and P. Lacerda, "The Kuiper belt and other debris disks," in Astrophysics in the Next Decade: The James Webb Space Telescope and Concurrent Facilities, Cham, Switzerland: Springer, 2009, pp. 53–100.
[19] M. Scheurer, G.-L. R. Anselmetti, O., Tailored, and N. C. R. M., "Tailored and externally corrected coupled cluster with quantum inputs," arXiv, vol. 2403.02240v3, Jun. 2024.
[20] T. G. Dietterich, "Overfitting and under computing in machine learning," ACM Computing Surveys, vol. 27, no. 3, pp. 129-130, Sep. 1995.
[21] J. R. Quinlan, "Induction of decision trees," Mach. Learn., vol. 1, no. 1, pp. 81–106, Mar. 1986.
[22] M. M. a. S. L. P. Rebentrost, "Quantum Support Vector Machine for Big Data Classification," Phys. Rev. Lett., vol. 113, no. 13, p. 130503, Sep. 2014.
[23] M. Biswal, "A Short Review on Machine Learning in Space Science and Exploration," UG Researcher, Department of Computer Science and Engineering, University College of Engineering Villupuram, India, 2024.
[24] M. S. H. a. M. A. Zabed, "Machine Learning Approaches for Classification and Diameter Prediction of Asteroids," in Advances in Space Research, vol. 12, pp. 123-145, Jan. 2023.
[25] J. B. e. al., "Quantum machine learning," Nature, vol. 549, pp. 195–202, Sep. 2017.
[26] M. Bhavin, S. Tanwar, N. Sharma, S. Tyagi, and N. Kumar, "Blockchain and quantum blind signature-based hybrid scheme for healthcare 5.0 applications," Journal of Information Security and Applications, vol. 56, Feb. 2021.
[27] V. Havlíček, A. D. Córcoles, K. Temme, A. W. Harrow, A. Kandala, J. M. Chow, J. M. Gambetta, and M. J. Biercuk, "Supervised learning with quantum-enhanced feature spaces," Nature, vol. 567, no. 7747, pp. 209–212, Mar. 2019.
[28] Y. Shalev-Shwartz and N. S. Shalev-Shwartz, "Pegasos: Primal estimated sub-gradient solver for SVM," in Proc. 24th Int. Conf. Mach. Learn., Jun. 2007.
[29] M. S. H. a. M. A. Zabed, "Machine learning approaches for classification and diameter prediction of asteroids," Singapore: Springer, 2023.
[31] W. Joon Yun, H. Baek, and J. Kim, ‘‘Projection valued measure-based quantum machine learning for multi-class classification,’’ 2022, arXiv:2210.16731.
[32] M.Schuld and N. Killoran, ‘‘Quantum machine learning in feature Hilbert spaces,’’ Phys. Rev. Lett., vol. 122, no. 4, Feb. 2019, Art. no. 040504.
[33] H. Park and Y.-C. Jeong, ‘‘Quantum algorithm for the two-body problem in quantum mechanics,’’ 2020, arXiv:2011.01319.
[34] A. Leverrier and M. Weigand, ‘‘Quantum algorithms for space situational awareness,’’ 2021, arXiv:2109.05728.
[35] E. Zahedinejad, M. Schuld, and N. Killoran, ‘‘Quantum variational algorithms for the Kepler problem,’’ 2020, arXiv:2012.09242.
[36] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, ‘‘Dropout: A simple way to prevent neural networks from overfitting,’’ J. Mach. Learn. Res., vol. 15, no. 56, pp. 1929–1958, 2014.
[37] Voosen, P. Earth may face a higher risk of catastrophic asteroid strikes. Science 2023, 379, 1179. CERES Project Website. Available online: http://cgis.utcluj.ro/ceres/ (accessed on 3 July 2023).
[38] M. M. Badža and M. Č. Barjaktarović, "Classification of brain tumors from MRI images using a convolutional neural network," Appl. Sci., vol. 10, no. 6, p. 1999, 2020.
[39] F. Rosenblatt, "The perceptron: A probabilistic model for information storage and organization in the brain," Psychol. Rev., vol. 65, no. 6, pp. 386–408, 1958.
[40] D. Gorgan, O. Vaduvescu, T. Stefanut, V. Bacu, A. Sabou, D. C. Balazs, C. I. Nandra, C. Boldea, A. L. Boldea, M. Predatu, et al., "NEARBY Platform for Automatic Asteroids Detection and EURONEAR Surveys," arXiv, 2019, arXiv:1903.03479.
[41] Takahashi, S.; Scheeres, D.J. Autonomous Reconnaissance Trajectory Guidance at Small Near-Earth Asteroids via Reinforcement Learning. J. Guid. Control Dyn. 2023, 46, 1280–1297.
[42] A. Penttilä, H. Hietala, and K. Muinonen, "Asteroid spectral taxonomy using neural networks," Astron. Astrophys., vol. 649, A46, 2021.
[43] K. A. Pearson, L. Palafox, and C. A. Griffith, "Searching for exoplanets using artificial intelligence," Mon. Not. R. Astron. Soc., vol. 474, pp. 478–491, 2017.
[44] M. Ansdell, Y. Ioannou, H. P. Osborn, M. Sasdelli, J. C. Smith, D. Caldwell, J. M. Jenkins, C. Räissi, D. Angerhausen, and 2018 NASA Frontier Development Lab Exoplanet Mentors, "Scientific domain knowledge improves exoplanet transit classification with deep learning," Astrophys. J., vol. 869, L7, 2018.
[45] Hora, K. Classifying Exoplanets as Potentially Habitable Using Machine Learning. In ICT Based Innovations; Saini, A.K., Nayak, A.K., Vyas, R.K., Eds.; Springer: Singapore, 2018; pp. 203–212.
[46] S. Jamal and J. S. Bloom, "On neural architectures for astronomical time-series classification with application to variable stars," Astrophys. J. Suppl. Ser., vol. 250, no. 30, 2020.
[47] J. D. Hefele, F. Bortolussi, and S. P. Zwart, "Identifying Earth-impacting asteroids using an artificial neural network," Astron. Astrophys., vol. 634, A45, 2020.
[48] C. R. Nugent, J. Dailey, R. M. Cutri, F. J. Masci, and A. K. Mainzer, "Machine learning and next-generation asteroid surveys," in Proceedings of the 49th AAS/Division for Planetary Sciences Meeting, Provo, UT, USA, 15–20 October 2017, p. 103.03.
[49] M. Lieu, L. Conversi, B. Altieri, and B. Carry, "Detecting Solar system objects with convolutional neural networks," Mon. Not. R. Astron. Soc., vol. 485, pp. 5831–5842, 2019.
[50] Y. Galindo and A. Lorena, "Deep Transfer Learning for Meteor Detection," in Anais do XV Encontro Nacional de Inteligência Artificial e Computational, Porto Alegre, RS, Brasil, 2018, pp. 528–537.
[51] A. Waszczak, T.A. Prince, R. Laher, F. Masci, B. Bue, U. Rebbapragada, T. Barlow, J. Surace, G. Helou, and S. Kulkarni, "Small Near-Earth Asteroids in the Palomar Transient Factory Survey: A Real-Time Streak-detection System," Publ. Astron. Soc. Pac., vol. 129, no. 034402, 2017.
[52] Lau, J.W.Z., Lim, K.H., Shrotriya, H. et al. NISQ computing: where are we and where do we go? AAPPS Bull. 32, 27 (2022). https://doi.org/10.1007/s43673-022-00058-z
[53] Bhavsar, R., Jadav, N. K., Bodkhe, U., & others. (2023). Classification of Potentially Hazardous Asteroids Using Supervised Quantum Machine Learning. IEEE Access, 11, 75829–75848. https://doi.org/10.1109/access.2023.3297498
[54] P. Rebentrost, M. Mohseni, & S. Lloyd, "Quantum support vector machine for big data classification," Physical Review Letters, vol. 113, no. 13, p. 130503, 2014.
[55] M. Biswal, "A Short Review on Machine Learning in Space Science and Exploration," UG Researcher, Department of Computer Science and Engineering, University College of Engineering Villupuram, India, 2024.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Kuldeep Vayadande, Yogesh Bodhe, Amol A. Bhosle, Kavita Sultanpure, Geetanjali Yadav, Ajit R. Patil, Jyoti Jayesh Chavhan, Amolkumar N. Jadhav, Preeti Bailke
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 3.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
