Open Access
Issue
E3S Web Conf.
Volume 726, 2026
The Second International Congress on Environment, Energy, and Materials for Sustainable Development Technology (IC2EM-SDT’26)
Article Number 01045
Number of page(s) 7
DOI https://doi.org/10.1051/e3sconf/202672601045
Published online 13 July 2026
  1. A. Singh et al., Delivering sustainable climate action: reframing the sustainable development goals, Npj Clim. Action 3, 110 (2024). [Google Scholar]
  2. OECD, A Quantum Technologies Policy Primer, OECD Digital Economy Papers, 2025. [Google Scholar]
  3. V. Raseena, Quantum computing: foundations, algorithms, and emerging applications, Front. Quantum Sci. Technol. 4, 1723319 (2025). [Google Scholar]
  4. A. Sivakumar, H. K Nair, A. Joshi, K.W.R.A.P Videsh, and R. M. P, A computational study and analysis of Variational Quantum Eigensolver over multiple parameters for molecules and ions, EPJ Quantum Technol. 11, 73 (2024). [Google Scholar]
  5. J. T. Seeley, M. J. Richard, and P. J. Love, The Bravyi-Kitaev transformation for quantum computation of electronic structure, The Journal of Chemical Physics 137, 224109 (2012). [Google Scholar]
  6. A. Peruzzo, J. McClean, P. Shadbolt, M.-H. Yung, X.-Q. Zhou, P. J. Love, A. Aspuru-Guzik, and J. L. O, Brien, A variational eigenvalue solver on a quantum processor, Nat Commun 5, 4213 (2014). [Google Scholar]
  7. G. Greene-Diniz, D. Z. Manrique, W. Sennane, Y. Magnin, E. Shishenina, P. Cordier, P. Llewellyn, M. Krompiec, M. J. Rancić, and D. M. Ramo, Modelling Carbon Capture on Metal-Organic Frameworks with Quantum Computing, arXiv:2203.15546. [Google Scholar]
  8. E. Farhi, J. Goldstone, S. Gutmann, and L. Zhou, The Quantum Approximate Optimization Algorithm and the Sherrington-Kirkpatrick Model at Infinite Size, Quantum 6, 759 (2022). [Google Scholar]
  9. M. Rahmati, Hybrid Quantum-Classical Optimization Algorithms for Energy-Efficient Smart Grids, Transactions on Environment and Electrical 7, 1 (2025). [Google Scholar]
  10. M. D. Sapova and A. K. Fedorov, Variational quantum eigensolver techniques for simulating carbon monoxide oxidation, Commun Phys 5, 199 (2022). [Google Scholar]
  11. G. R. Dahale, Quantum Simulations for Carbon Capture on Metal-Organic Frameworks, in 2023 IEEE International Conference on Quantum Computing and Engineering (QCE) (IEEE, Bellevue, WA, USA, 2023), pp. 89–93. [Google Scholar]
  12. H. P. Paudel, M. Syamlal, S. E. Crawford, Y.-L. Lee, R. A. Shugayev, P. Lu, P. R. Ohodnicki, D. Mollot, and Y. Duan, Quantum Computing and Simulations for Energy Applications: Review and Perspective, ACS Eng. Au 2, 151 (2022). [Google Scholar]
  13. A. Ajagekar and F. You, Variational quantum circuit learning-enabled robust optimization for AI data center energy control and decarbonization, Advances in Applied Energy 14, 100179 (2024). [Google Scholar]
  14. S. Luo, D. Qiu, L. Tang, H. Wang, Q. Xiang, and L. Zhao, Smart Grid NOMA Resource Allocation Optimization Using Quantum Approximate Techniques, in 2025 4th Workshop on Electronics Communication Engineering (WECE) (IEEE, Hefei, China, 2025), pp. 94–98. [Google Scholar]
  15. M. K. K, M. M, A. B. S, Mahadevi, F. Tlajiya, and S. Chavan, Optimizing Energy Management and Load Balancing Through AI-Driven Quantum Approximate Optimization, in 2024 4th International Conference on Mobile Networks and Wireless Communications (ICMNWC) (IEEE, Tumkuru, India, 2024), pp. 1–7. [Google Scholar]
  16. R. Shaydulin et al., Evidence of Scaling Advantage for the Quantum Approximate Optimization Algorithm on a Classically Intractable Problem, Sci. Adv. 10, eadm6761 (2024). [Google Scholar]
  17. H. Jing, Y. Wang, and Y. Li, Data-driven quantum approximate optimization algorithm for power systems, Commun Eng 2, 12 (2023). [Google Scholar]
  18. A. R. Jami and A. Haleem, Quantum computing as an enabler for sustainable circular economy implementation in Industry 4.0: A study, Human Settlements and Sustainability 1, 103 (2025). [Google Scholar]
  19. F. Arute et al., Quantum supremacy using a programmable superconducting processor, Nature 574, 505 (2019). [CrossRef] [PubMed] [Google Scholar]
  20. J. R. McClean, J. Romero, R. Babbush, and A. Aspuru-Guzik, The theory of variational hybrid quantum-classical algorithms, New J. Phys. 18, 023023 (2016). [Google Scholar]
  21. E. Farhi, J. Goldstone, and S. Gutmann, A Quantum Approximate Optimization Algorithm, arXiv:1411.4028. [Google Scholar]
  22. D. Root, Quantum Technologies in the Context of Climate Change: Emphasizing Sustainability in a Responsible Innovation Approach to Quantum Innovation, Nanoethics 19, 4 (2025). [Google Scholar]
  23. K. T. M. Ho, K.-C. Chen, L. Lee, F. Burt, S. Yu, and P.-H. Lee, Quantum Computing for Climate Resilience and Sustainability Challenges, in 2024 IEEE International Conference on Quantum Computing and Engineering (QCE) (IEEE, Montreal, QC, Canada, 2024), pp. 262–267. [Google Scholar]
  24. A. Callison and N. Chancellor, Hybrid quantum-classical algorithms in the noisy intermediate-scale quantum era and beyond, Phys. Rev. A 106, 010101 (2022). [Google Scholar]
  25. M. Fellous-Asiani, J. H. Chai, Y. Thonnart, H. K. Ng, R. S. Whitney, and A. Auffèves, Optimizing Resource Efficiencies for Scalable Full-Stack Quantum Computers, PRX Quantum 4, 040319 (2023). [Google Scholar]
  26. V. Sood and R. P. Chauhan, Progress and prospects of quantum computing in sustainable development: An analytical review, Expert Systems 41, e13389 (2024). [Google Scholar]

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