Ijraset Journal For Research in Applied Science and Engineering Technology
Authors: Mr. D. Venkatabramhanaidu, Dr. Annam Sreenivasulu
DOI Link: https://doi.org/10.22214/ijraset.2025.74308
Certificate: View Certificate
Quantum computing represents a paradigm shift in computational capabilities with profound implications for electrical engineering. This article examines the transformative potential of quantum technologies across various electrical engineering domains, including power systems optimization, electronic design automation, signal processing, and communications. Through analysis of current implementations and theoretical frameworks, we demonstrate how quantum algorithms are already enhancing solutions to previously intractable problems in electrical engineering. The article also addresses the challenges and future directions for quantum computing integration into electrical engineering workflows, highlighting the growing importance of quantum-classical hybrid systems.
Electrical engineering is undergoing a major transformation driven by advances in quantum computing, which leverages principles like superposition, entanglement, and quantum interference to process information in ways classical computers cannot. Quantum computers promise exponential speedups in optimization, simulation, and signal processing tasks crucial for electrical engineering.
As of 2024, quantum technologies are moving from theory to practice, with significant investments and a rapidly growing market projected to reach $1.7 billion by 2026. The article reviews quantum computing applications across key areas: fundamental principles, power systems, electronic design, signal processing, communications, and hardware.
Fundamentally, qubits differ from classical bits by existing in superpositions, enabling massive parallelism. Various qubit technologies—superconducting circuits, trapped ions, photonics, and quantum dots—each have unique trade-offs in coherence time, operational conditions, and scalability. Entanglement links qubits to enable quantum parallelism, which allows simultaneous evaluation of many solutions, offering dramatic computational advantages.
In power systems, quantum computing aids grid optimization, stability analysis, and integration of renewables by solving complex scheduling and load balancing problems faster than classical methods. Real-world applications include Dubai’s quantum-optimized grid, which improved solar energy utilization. Quantum simulations also accelerate development of advanced battery materials for energy storage, critical for renewable integration and electric vehicles.
For electronic design automation (EDA), quantum algorithms help optimize complex circuit layouts and parameters, overcoming limits faced by classical tools as device scales shrink. Quantum computing also advances semiconductor device development by simulating materials and quantum effects at atomic levels with high accuracy, enabling better design of emerging wide-bandgap semiconductors used in power electronics.
Overall, quantum computing presents transformative opportunities for electrical engineering, although challenges remain in hardware development, error correction, and practical scalability.
Quantum computing represents a paradigm shift for electrical engineering, offering unprecedented computational power for solving complex problems across diverse domains [77]. From optimizing power grids and designing advanced semiconductors to processing signals with quantum algorithms and developing novel control systems, quantum technologies are transforming electrical engineering practice [78]. The applications discussed in this article demonstrate that quantum computing is no longer merely theoretical but is delivering practical value in real-world engineering systems [79]. As hardware continues to improve and algorithms become more sophisticated, this trend is expected to accelerate, making quantum capabilities increasingly accessible to electrical engineers [80]. While challenges remain in terms of qubit stability, error correction, and system integration, the rapid pace of innovation suggests these hurdles will be overcome in the coming years [81]. The synergistic relationship between electrical engineering and quantum computing—where each field advances the other—creates a virtuous cycle of innovation with far-reaching implications [82]. As we look to the future, quantum computing is poised to become an indispensable tool in the electrical engineering arsenal, enabling solutions to some of humanity\'s most pressing technological challenges [83]. Electrical engineers who embrace these technologies today will be at the forefront of this transformation, shaping the future of technology and society through quantum-enhanced innovation [84].
[1] Nielsen, M. A., & Chuang, I. L. (2010). Quantum Computation and Quantum Information: 10th Anniversary Edition. Cambridge University Press. [2] Preskill, J. (2018). Quantum Computing in the NISQ era and beyond. Quantum, 2, 79. [3] MarketsandMarkets. (2023). Quantum Computing Market by Offering, Deployment, Application, Technology, End-User Industry and Region - Global Forecast to 2026. MarketsandMarkets Research Private Ltd. [4] National Academies of Sciences, Engineering, and Medicine. (2019). Quantum Computing: Progress and Prospects. The National Academies Press. [5] DiVincenzo, D. P. (2000). The physical implementation of quantum computation. Fortschritte der Physik, 48(9-11), 771-783. [6] Aaronson, S. (2013). Quantum Computing Since Democritus. Cambridge University Press. [7] Ladd, T. D., Jelezko, F., Laflamme, R., Nakamura, Y., Monroe, C., & O\'Brien, J. L. (2010). Quantum computers. Nature, 464(7285), 45-53. [8] Horodecki, R., Horodecki, P., Horodecki, M., & Horodecki, K. (2009). Quantum entanglement. Reviews of Modern Physics, 81(2), 865 [9] Deutsch, D. (1985). Quantum theory, the Church–Turing principle and the universal quantum computer. Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences, 400(1818), 97-117. [10] Shor, P. W. (1999). Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM Review, 41(2), 303-332. [11] Grover, L. K. (1996). A fast quantum mechanical algorithm for database search. Proceedings of the Twenty-Eighth Annual ACM Symposium on Theory of Computing, 212-219. [12] Farhi, E., Goldstone, J., & Gutmann, S. (2014). A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028. [13] Johnson, M. W., et al. (2011). Quantum annealing with manufactured spins. Nature, 473(7346), 194-198. [14] Ajagekar, A., & You, F. (2019). Quantum computing for energy systems optimization: Challenges and opportunities. Energy, 179, 76-89. [15] Dubai Electricity and Water Authority. (2023). DEWA\'s Quantum Computing Initiative: Annual Report 2023. DEWA Publications. [16] O\'Malley, D., Vesselinov, V. V., Alexandrov, B. S., & Alexandrov, L. B. (2018). Nonnegative/binary matrix factorization with a D-Wave quantum annealer. PLoS One, 13(12), e0206653 [17] Cao, Y., et al. (2019). Quantum chemistry in the age of quantum computing. Chemical Reviews, 119(19), 10856-10915. [18] McArdle, S., Endo, S., Aspuru-Guzik, A., Benjamin, S. C., & Yuan, X. (2020). Quantum computational chemistry. Reviews of Modern Physics, 92(1), 015003. [19] Microsoft Quantum Team. (2023). Accelerating Materials Discovery with Quantum Computing: Microsoft\'s Approach. Microsoft Research Blog. [20] Bardhan, A., et al. (2023). Quantum computational analysis of lithium-sulfur battery chemistry. Advanced Energy Materials, 13(15), 2203456 [21] Brown, K. L., Munro, W. J., & Kendon, V. M. (2010). Using quantum computers for quantum simulation. Entropy, 12(11), 2268-2307 [22] Stamatopoulos, N., et al. (2020). Option pricing using quantum computers. Quantum, 4, 291. [23] Moore, G. E. (1965). Cramming more components onto integrated circuits. Electronics, 38(8), 114-117. [24] Markov, I. L. (2014). Limits on fundamental limits to computation. Nature, 512(7513), 147-154. [25] Kahng, A. B. (2018). The ITRS design technology and system drivers roadmap: Process and status. In 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE) (pp. 459-464). IEEE. [26] Venturelli, D., & Kondratyev, A. (2019). Reverse quantum annealing approach to portfolio optimization problems. Quantum Machine Intelligence, 1(1-2), 17-30. [27] IBM Quantum. (2023). Quantum-enhanced Electronic Design Automation: IBM\'s Research Initiatives. IBM Research Publications. [28] Li, G., Ding, Y., & Xie, Y. (2019). Tackling the qubit mapping problem for NISQ-era quantum devices. In Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems (pp. 1001-1014) [29] Zhou, L., Wang, S. T., Choi, S., Pichler, H., & Lukin, M. D. (2020). Quantum approximate optimization algorithm: Performance, mechanism, and implementation on near-term devices. Physical Review X, 10(2), 021067 [30] Aspuru-Guzik, A., Dutoi, A. D., Love, P. J., & Head-Gordon, M. (2005). Simulated quantum computation of molecular energies. Science, 309(5741), 1704-1707. [31] Kassal, I., Whitfield, J. D., Perdomo-Ortiz, A., Yung, M. H., & Aspuru-Guzik, A. (2011). Simulating chemistry using quantum computers. Annual Review of Physical Chemistry, 62, 185-207. [32] Reiher, M., Wiebe, N., Svore, K. M., Wecker, D., & Troyer, M. (2017). Elucidating reaction mechanisms on quantum computers. Proceedings of the National Academy of Sciences, 114(29), 7555-7560 [33] Baliga, B. J. (2010). Advanced power MOSFET concepts. Springer Science & Business Media. [34] Shehabi, A., et al. (2018). United States data center energy usage report. Lawrence Berkeley National Laboratory. [35] Oppenheim, A. V., & Schafer, R. W. (2010). Discrete-Time Signal Processing. Pearson Education. [36] Lloyd, S. (1996). Universal quantum simulators. Science, 273(5278), 1073-1078. [37] Cleve, R., Ekert, A., Macchiavello, C., & Mosca, M. (1998). Quantum algorithms revisited. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 454(1969), 339-354. [38] Haykin, S. (2013). Adaptive Radar Signal Processing. John Wiley & Sons. [39] Biamonte, J., et al. (2017). Quantum machine learning. Nature, 549(7671), 195-202. [40] Schuld, M., Sinayskiy, I., & Petruccione, F. (2015). An introduction to quantum machine learning. Contemporary Physics, 56(2), 172-185. [41] Havlí?ek, V., et al. (2019). Supervised learning with quantum-enhanced feature spaces. Nature, 567(7747), 209-212. [42] Cong, I., Choi, S., & Lukin, M. D. (2019). Quantum convolutional neural networks. Nature Physics, 15(12), 1273-1278. [43] Gisin, N., Ribordy, G., Tittel, W., & Zbinden, H. (2002). Quantum cryptography. Reviews of Modern Physics, 74(1), 145. [44] Pirandola, S., et al. (2020). Advances in quantum cryptography. Advances in Optics and Photonics, 12(4), 1012-1236. [45] Princeton University Quantum Communications Lab. (2023). Quantum-Enhanced Signal Processing for 6G Networks. Technical Report PUCQ-2023-04. [46] Saad, W., Bennis, M., & Chen, M. (2019). A vision of 6G wireless systems: Applications, trends, technologies, and open research problems. IEEE Network, 34(3), 134-142. [47] Bennett, C. H., & Brassard, G. (2014). Quantum cryptography: Public key distribution and coin tossing. Theoretical Computer Science, 560, 7-11. [48] Terra Quantum AG. (2023). Quantum Key Distribution: Implementation Report 2023. Terra Quantum Technical Publications. [49] Kimble, H. J. (2008). The quantum internet. Nature, 453(7198), 1023-1030. [50] Shor, P. W. (1995). Scheme for reducing decoherence in quantum computer memory. Physical Review A, 52(4), R2493. [51] Terhal, B. M. (2015). Quantum error correction for quantum memories. Reviews of Modern Physics, 87(2), 307. [52] Devoret, M. H., & Schoelkopf, R. J. (2013). Superconducting circuits for quantum information: An outlook. Science, 339(6124), 1169-1174. [53] Microsoft Quantum. (2023). Majorana-based Quantum Computing: Progress Report 2023. Microsoft Research Publications. [54] Ofek, N., et al. (2016). Extending the lifetime of a quantum bit with error correction in superconducting circuits. Nature, 536(7617), 441-445. [55] Krantz, P., et al. (2019). A quantum engineer\'s guide to superconducting qubits. Applied Physics Reviews, 6(2), 021318. [56] Reagor, M., et al. (2018). Demonstration of universal parametric entangling gates on a multi-qubit lattice. Science Advances, 4(2), eaao3603. [57] Hornibrook, J. M., et al. (2015). Cryogenic control architecture for large-scale quantum computing. Physical Review Applied, 3(2), 024010. [58] IBM Research. (2023). Quantum Control Systems: Hardware and Software Co-Design. IBM Journal of Research and Development, 67(3/4), 8:1-8:12. [59] Córcoles, A. D., et al. (2015). Demonstration of a quantum error detection code using a square lattice of four superconducting qubits. Nature Communications, 6(1), 6979. [60] Wiseman, H. M., & Milburn, G. J. (2009). Quantum Measurement and Control. Cambridge University Press. [61] Campbell, E. T., Terhal, B. M., & Vuillot, C. (2017). Roads towards fault-tolerant universal quantum computation. Nature, 549(7671), 172-179. [62] Preskill, J. (2012). Quantum computing and the entanglement frontier. arXiv preprint arXiv:1203.5813. [63] Fowler, A. G., Mariantoni, M., Martinis, J. M., & Cleland, A. N. (2012). Surface codes: Towards practical large-scale quantum computation. Physical Review A, 86(3), 032324. [64] Jones, N. (2018). The information factories. Nature, 561(7722), 163-166. [65] Shehabi, A., et al. (2016). Energy efficiency of quantum computing systems. In 2016 IEEE International Electron Devices Meeting (IEDM) (pp. 34.3.1-34.3.4). IEEE. [66] Maruyama, K., Nori, F., & Vedral, V. (2009). Colloquium: The physics of Maxwell\'s demon and information. Reviews of Modern Physics, 81(1), 1. [67] McClean, J. R., Romero, J., Babbush, R., & Aspuru-Guzik, A. (2016). The theory of variational hybrid quantum-classical algorithms. New Journal of Physics, 18(2), 023023. [68] Peruzzo, A., et al. (2014). A variational eigenvalue solver on a photonic quantum processor. Nature Communications, 5(1), 4213. [69] Amazon Web Services. (2023). AWS Braket Hybrid Jobs: Integrating Quantum and Classical Compute. AWS Quantum Technologies Blog. [70] Cerezo, M., et al. (2021). Variational quantum algorithms. Nature Reviews Physics, 3(9), 625-644. [71] Arute, F., et al. (2019). Quantum supremacy using a programmable superconducting processor. Nature, 574(7779), 505-510. [72] National Science and Technology Council. (2022). National Strategic Overview for Quantum Information Science. Executive Office of the President. [73] Quantum Economic Development Consortium. (2023). Quantum Workforce Report 2023. QED-C Publications. [74] Massachusetts Institute of Technology. (2023). MIT Quantum Curriculum Initiative: 2023 Progress Report. MIT Open Learning Publications. [75] IBM Corporation. (2023). IBM Quantum Certification Program: Curriculum and Outcomes. IBM Training and Skills Development. [76] QuEra Computing. (2023). Professional Certification in Quantum Error Mitigation: Program Results. QuEra Technical Publications. [77] Dowling, J. P., & Milburn, G. J. (2003). Quantum technology: the second quantum revolution. Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 361(1809), 1655-1674. [78] Flamini, F., Spagnolo, N., & Sciarrino, F. (2019). Photonic quantum information processing: a review. Reports on Progress in Physics, 82(1), 016001. [79] Acín, A., et al. (2018). The quantum technologies roadmap: a European community view. New Journal of Physics, 20(8), 080201. [80] Georgescu, I. M., Ashhab, S., & Nori, F. (2014). Quantum simulation. Reviews of Modern Physics, 86(1), 153. [81] Bruzewicz, C. D., Chiaverini, J., McConnell, R., & Sage, J. M. (2019). Trapped-ion quantum computing: Progress and challenges. Applied Physics Reviews, 6(2), 021314. [82] Monroe, C., & Kim, J. (2013). Scaling the ion trap quantum processor. Science, 339(6124), 1164-1169. [83] Awschalom, D. D., et al. (2021). Development of quantum engineering. IEEE Spectrum, 58(4), 36-41. [84] Oliver, W. D., & Welander, P. B. (2013). Materials in superconducting quantum bits. MRS Bulletin, 38(10), 816-825.
Copyright © 2025 Mr. D. Venkatabramhanaidu, Dr. Annam Sreenivasulu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Paper Id : IJRASET74308
Publish Date : 2025-09-19
ISSN : 2321-9653
Publisher Name : IJRASET
DOI Link : Click Here