Quasar: Quantum-Accelerated Sustainable Anomaly Recognition in Climate Systems

Year : 2025 | Volume : 2 | 02 | Page :
    By

    Himadri Sekhar Mondal,

  • Ranjit Haldar2,

  • Asim Kumar Panda,

  • Hrishikesh Kumar Chaudhary,

  • Shuvojit Das,

  1. Assistant Professor, Department of CSE, MCKV Institute of Engineering, Howrah, West Bengal, India
  2. Assistant Professor, Department of IT, B.P Poddar Institute of Management and Technology, Kolkata, West Bengal, India
  3. Assistant Professor, Department of IT, MCKV Institute of Engineering, Howrah, West Bengal, India
  4. Student, Department of IT, MCKV Institute of Engineering, Howrah, West Bengal, India
  5. Student, Department of AI-ML, Birla Institute of Technology and Science, Pilani, Rajasthan, India

Abstract

Accurate detection of climate anomalies is vital for disaster alleviation and policy making in a sustainable manner, but customary detection methods face the challenges of computational inefficiency and physical inconsistency. In this study, we propose a novel approach called Quantum-Optimized Fuzzy Physics-Informed Neural Networks (QFuzzy-PINNs), which integrates quantum computing, fuzzy logic, and physics-informed deep learning. As a first step, we employ quantum annealing for conventional optimization to adjust multiple Gaussian membership function parameters in the fuzzy inference system to achieve optimal values, thereby representing meteorological uncertainties in an improved and computationally efficient manner. Second, to ensure that model predictions of physical quantities are consistent, a variational quantum eigensolver (VQE) is used to dynamically adjust the weights of competing physics-based loss terms governed by fluid dynamics and heat transfer partial differential equations. Experiments conducted with high-resolution Indian Meteorological Department datasets validate the effectiveness of QFuzzy-PINNs, which exhibited 20.7% improved anomaly detection performance with reduced MAE from 1.5580 to 1.2355 when compared to classical Fuzzy-PINNs while delivering a 47.3% decrease in required computational energy, which is intrinsic for real-time scaling. Although the model’s transparent fuzzy rules incur a minor interpretative cost, the enforcement of PDE constraints is reconciled with quantum technology, ensuring rigorous adherence to the laws of physics. This bridges early research on quantum technologies with climate applications, providing an energy-efficient, scalable, and physically consistent architecture for next-generation real-time weather forecasting and climate analysis, with unrivaled adaptability to changing conditions.

Keywords: Quantum Machine Learning, Fuzzy-PINN, Climate Anomaly Detection, Energy Efficiency, Quantum Annealing, Physics-Informed AI, Fuzzy, PINN, Neural Network, Gaussian Membership, Error Reduction, Sustainable

How to cite this article:
Himadri Sekhar Mondal, Ranjit Haldar2, Asim Kumar Panda, Hrishikesh Kumar Chaudhary, Shuvojit Das. Quasar: Quantum-Accelerated Sustainable Anomaly Recognition in Climate Systems. International Journal of Climate Conditions. 2026; 02(02):-.
How to cite this URL:
Himadri Sekhar Mondal, Ranjit Haldar2, Asim Kumar Panda, Hrishikesh Kumar Chaudhary, Shuvojit Das. Quasar: Quantum-Accelerated Sustainable Anomaly Recognition in Climate Systems. International Journal of Climate Conditions. 2026; 02(02):-. Available from: https://journals.stmjournals.com/ijcc/article=2026/view=242193


References

  1. K. Kashinath, M. Mustafa, A. Albert, J. Wu, C. Jiang, S. Esmaeilzadeh, K. Azizzadenesheli, R. Wang, A. Chattopadhyay, A. Singh et al., ―Physics-informed machine learning: Case studies for weather and climate modelling,‖ Philosophical Transactions of the Royal Society A, vol. 379, no. 2194, p. 20200093, 2021
  2. Cuomo S, Di Cola VS, Giampaolo F, Rozza G, Raissi M, Piccialli F. Scientific machine learning through physics–informed neural networks: Where we are and what’s next. Journal of Scientific Computing. 2022 Sep;92(3):88. file:///C:/Users/Pc32/Downloads/s10915-022-01939-z.pdf
  3. Molina MJ, O’Brien TA, Anderson G, Ashfaq M, Bennett KE, Collins WD, Dagon K, Restrepo JM, Ullrich PA. A review of recent and emerging machine learning applications for climate variability and weather phenomena. Artificial Intelligence for the Earth Systems. 2023 Oct;2(4):220086
  4. Raissi, P. Perdikaris, and G. E. Karniadakis, “Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,” Journal of Computational Physics, vol. 378, pp. 686–707, 2019.
  5. -E. Choe, H. Kim, S. Park, J. Lee, M. Kim, and R. Park, “Physics-informed deep neural network embedded in a chemical transport model for the Amazon rainforest,” npj Climate and Atmospheric Science, vol. 6, no. 1, pp. 1–10, 2023.
  6. Feng, K. Fang, and C. Shen, “Physics-informed neural networks of the Saint-Venant equations for downscaling a large-scale river model,” Water Resources Research, vol. 59, no. 3, p. e2022WR032342, 2023.
  7. Mei, J. Zhao, Q.-S. Li, Z.-Y. Chen, J.-J. Zhang, Q. Wang, Y.-C. Wu, and G.-P. Guo, “Particle swarm optimization for a variational quantum eigensolver,” Physical Chemistry Chemical Physics, vol. 26, no. 46, pp. 29070–29081, 2024.
  8. Lubasch, J. Joo, P. Moinier, M. Kiffner, and D. Jaksch, “Variational quantum algorithms for nonlinear problems,” Physical Review A, vol. 101, no. 1, p. 010301, 2020.
  9. Ahmad and S. Jas, “Quantum-inspired neural networks for time-series air pollution prediction and control of the most polluted region in the world,” Quantum Machine Intelligence, vol. 7, no. 1, pp. 1–13, 2025.
  10. Rao, “Performance study of variational quantum linear solver with an improved ansatz for reservoir flow equations,” Physics of Fluids, vol. 36, no. 4, 2024.
  11. Xie, H. Yan, Y. Lu, and L. Zeng, “Simulating field soil temperature variations with physics-informed neural networks,” Soil and Tillage Research, vol. 244, p. 106236, 2024.
  12. Nguyen, J. Brandstetter, A. Kapoor, J. K. Gupta, and A. Grover, “ClimODE: Climate and weather forecasting with physics-informed neural ODEs,” arXiv preprint arXiv:2304.07482, 2023.
  13. -C. Hsu, N.-Y. Chen, T.-Y. Li, P.-H. H. Lee, and K.-C. Chen, “Quantum kernel-based long short-term memory for climate time-series forecasting,” in 2025 International Conference on Quantum Communications, Networking, and Computing (QCNC). IEEE, 2025, pp. 421–426.
  14. -C. Akazan, V. R. Mbingui, G. L. R. N’guessan, and I. Karambal, “Localized weather prediction using Kolmogorov-Arnold network-based models and deep RNNs,” arXiv preprint arXiv:2505.17178, 2025.
Ahead of Print Subscription Original Research
Volume 02
02
Received 10/01/2026
Accepted 12/01/2026
Published 13/02/2026
Publication Time 34 Days
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