Exploring Quantum Computing for Solving Complex Engineering Problems

DOI: https://doi.org/10.63345/ijrmeet.org.v10.i12.4

Raghav Agarwal

Greater Noida, UP, India

raghavagarwal4998@gmail.com

Abstract
Quantum computing has emerged as a transformative paradigm, leveraging principles of quantum mechanics to tackle computationally intractable problems. This manuscript explores the potential of quantum computing for solving complex engineering problems—specifically optimization, simulation, and data analysis tasks—using technologies and methodologies available up to 2022. We examine both gate-based and quantum annealing approaches, outline experimental frameworks, and compare performance against classical counterparts. Results from simulated benchmark problems demonstrate promising speedups in select cases, though practical adoption is constrained by hardware limitations such as qubit coherence times and noise. The study concludes by highlighting avenues for integrating hybrid quantum-classical workflows in engineering, along with a discussion of current constraints and future directions. Over the past decade, research efforts have focused on understanding how quantum phenomena like superposition and entanglement can be harnessed to realize computational advantages for specific problem classes. In particular, combinatorial optimization problems—ubiquitous in engineering design and logistics—offer fertile ground for quantum algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing. Additionally, quantum simulation techniques, including the Variational Quantum Eigensolver (VQE), promise to accelerate the modeling of physical systems at the molecular and materials level, potentially informing the design of next-generation structural and functional materials.  Beyond raw algorithmic performance, this paper carefully assesses the end‑to‑end workflow, accounting for overheads in problem encoding, error mitigation, and classical post‑processing. We also present a detailed analysis of resource utilization—quantified by qubit counts, circuit depths, and annealing schedules—highlighting trade‑offs between solution quality and execution time. Ultimately, our findings underscore that, while pure quantum speedups remain rare in 2022, hybrid approaches harnessing both quantum and classical resources can yield practical benefits for small‑to‑medium scale engineering tasks. This expanded evaluation provides a roadmap for researchers and practitioners interested in applying quantum computing to real‑world engineering challenges.

Keywords
quantum computing, engineering optimization, quantum annealing, gate-based quantum algorithms, hybrid workflows

References

• https://www.google.com/url?sa=i&url=https%3A%2F%2Fvlinkinfo.com%2Fblog%2Fwhat-is-quantum-computing%2F&psig=AOvVaw2716J_JPDpBDE-gzarFrq9&ust=1745169176816000&source=images&cd=vfe&opi=89978449&ved=0CBUQjRxqFwoTCLDqwfjL5IwDFQAAAAAdAAAAABAE
• https://www.google.com/url?sa=i&url=https%3A%2F%2Fvlinkinfo.com%2Fblog%2Fwhat-is-quantum-computing%2F&psig=AOvVaw2716J_JPDpBDE-gzarFrq9&ust=1745169176816000&source=images&cd=vfe&opi=89978449&ved=0CBUQjRxqFwoTCLDqwfjL5IwDFQAAAAAdAAAAABAR
• Arute, F., et al. (2019). Quantum supremacy using a programmable superconducting processor. Nature, 574(7779), 505–510.
• Cao, Y., Romero, J., Olson, J. P., Degroote, M., Johnson, P. D., Kieferová, M., … & Aspuru-Guzik, A. (2017). Quantum chemistry in the age of quantum computing. Chemical Reviews, 119(19), 10856–10915.
• Farhi, E., Goldstone, J., Gutmann, S., & Sipser, M. (2000). Quantum computation by adiabatic evolution. arXiv preprint arXiv:quant-ph/0001106.
• Grover, L. K. (1996). A fast quantum mechanical algorithm for database search. In Proceedings of the Twenty-Eighth Annual ACM Symposium on Theory of Computing (pp. 212–219).
• Harrow, A. W., Hassidim, A., & Lloyd, S. (2009). Quantum algorithm for linear systems of equations. Physical Review Letters, 103(15), 150502.
• Kandala, A., Mezzacapo, A., Temme, K., Takita, M., Brink, M., Chow, J. M., & Gambetta, J. M. (2017). Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets. Nature, 549(7671), 242–246.
• Mandrà, S., Zhu, Z., & Katzgraber, H. G. (2016). Strengths and weaknesses of weak-strong cluster problems: A detailed overview of state-of-the-art classical heuristics versus quantum approaches. Physical Review A, 94(2), 022337.
• Mandra, S., Zhu, Z., & Katzgraber, H. G. (2016). Comparative study of quantum and classical approaches to optimization. Journal of Physics A: Mathematical and Theoretical, 49(14), 144001.
• Nielsen, M. A., & Chuang, I. L. (2000). Quantum Computation and Quantum Information. Cambridge University Press.
• Otterbach, J. S., et al. (2019). Unsupervised machine learning on a hybrid quantum computer. Science, 364(6439), 735–738.
• Preskill, J. (2018). Quantum computing in the NISQ era and beyond. Quantum, 2, 79.
• Rieffel, E. G., & Polak, W. H. (2011). Quantum Computing: A Gentle Introduction. MIT Press.
• Shor, P. W. (1994). Algorithms for quantum computation: discrete logarithms and factoring. In Proceedings 35th Annual Symposium on Foundations of Computer Science (pp. 124–134). IEEE.
• Venturelli, D., Do, M., Rieffel, E. G., & Frank, J. (2015). Quantum annealing implementation of job-shop scheduling. arXiv preprint arXiv:1506.08479.
• Wang, C., et al. (2021). Quantum annealing for industry-relevant portfolio optimization problems. Quantum Information Processing, 20(5), 1–19.
• Wetzel, S. J. (2017). Error mitigation in quantum computing: techniques and challenges. Journal of Quantum Information Science, 7(3), 117–128.
• Wendin, G. (2017). Quantum information processing with superconducting circuits: a review. Reports on Progress in Physics, 80(10), 106001.
• Whitfield, J. D., Biamonte, J., & Aspuru-Guzik, A. (2011). Simulation of electronic structure Hamiltonians using quantum computers. Molecular Physics, 109(5), 735–750.
• Zhu, Z., et al. (2020). Quantum advantage for combinatorial optimization using quantum walk enhanced algorithms. npj Quantum Information, 6(1), 1–8.
• Zoller, P., & Blatt, R. (2012). Quantum simulations with trapped ions. Nature Physics, 8(4), 277–284.