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Quantum computing in civil engineering: Potentials and Limitations

Published 22 Feb 2024 in cs.ET | (2402.14556v2)

Abstract: Quantum computing is a new computational paradigm with the potential to solve certain computationally challenging problems much faster than traditional approaches. Civil engineering encompasses many computationally challenging problems, which leads to the question of how well quantum computing is suitable for solving civil engineering problems and how much impact and implications to the field of civil engineering can be expected when deploying quantum computing for solving these problems. To address these questions, we will, in this paper, first introduce the fundamentals of quantum computing. Thereupon, we will analyze the problem classes to elucidate where quantum computing holds the potential to outperform traditional computers and, focusing on the limitations, where quantum computing is not considered the most suitable solution. Finally, we will review common complex computation use cases in civil engineering and evaluate the potential and the limitations of being improved by quantum computing.

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References (82)
  1. Quantum Optimization: Potential, Challenges, and the Path Forward. arXiv e-prints, art. arXiv:2312.02279, Dec. 2023.
  2. Mining building performance data for energy-efficient operation. Advanced Engineering Informatics, 25(2):341–354, 2011.
  3. Quantum clustering algorithms. In ICML, pages 1–8, 2007.
  4. A. Ajagekar and F. You. Quantum computing for energy systems optimization: Challenges and opportunities. Energy, 179:76–89, 2019.
  5. Quantum computing based hybrid solution strategies for large-scale discrete-continuous optimization problems. Computers & Chemical Engineering, 132:106630, 2020.
  6. Computational fluid mechanics and heat transfer. 2020.
  7. Efficient extraction of insights at the edges of distributed systems. In IEEE BigData, 2023.
  8. Industry quantum computing applications. EPJ Quantum Technology, 8(1):25, 2021.
  9. Optimization concepts and applications in engineering. 2019.
  10. M. P. Bendsoe and O. Sigmund. Topology optimization: theory, methods, and applications. 2003.
  11. D. W. Berry. High-order quantum algorithm for solving linear differential equations. Journal of Physics A: Mathematical and Theoretical, 47(10):105301, 2014.
  12. Quantum error mitigation. Reviews of Modern Physics, 95(4):045005, 2023.
  13. Quantum chemistry in the age of quantum computing. Chemical reviews, 119(19), 2019.
  14. Semiconductor qubits in practice. Nature Reviews Physics, 3(3):157–177, 2021.
  15. Data-driven predictive maintenance planning framework for MEP components based on BIM and IoT using machine learning algorithms. Automation in Construction, 112, 2020.
  16. A Practical Guide to Quantum Machine Learning and Quantum Optimization: Hands-on Approach to Modern Quantum Algorithms. 2023.
  17. Practical quantum advantage in quantum simulation. Nature, 607(7920):667–676, 2022.
  18. B. M. Das. Principles of geotechnical engineering. 2021.
  19. Solving machine learning optimization problems using quantum computers. In Disruptive Technologies in Information Sciences IV, volume 11419, pages 60–69, 2020.
  20. P. Date and T. Potok. Adiabatic quantum linear regression. Scientific reports, 11(1):21905, 2021.
  21. A classical-quantum hybrid approach for unsupervised probabilistic machine learning. In Future of Inf. and Com. Conf. (FICC), pages 98–117, 2020.
  22. D. Deutsch. Quantum theory, the church–turing principle and the universal quantum computer. Proc. of the Royal Society of London. A. Mathematical and Physical Sciences, 400(1818):97–117, 1985.
  23. Quantum reinforcement learning. IEEE Trans. on Systems, Man, and Cybernetics, Part B (Cybernetics), 38(5):1207–1220, 2008.
  24. Optimal quantum phase estimation. Physical review letters, 102(4):040403, 2009.
  25. Quantum computing for finance: State-of-the-art and future prospects. IEEE Trans. on Quantum Engineering, 1:1–24, 2020.
  26. Covering problems in facility location: A review. Computers & Industrial Engineering, 62(1):368–407, 2012.
  27. Machine learning methods in quantum computing theory. Int. Conf. on Micro- And Nano-Electronics 2018, 2019. 10.1117/12.2522427.
  28. Machine learning algorithms in civil structural health monitoring: A systematic review. Archives of computational methods in engineering, 28, 2021.
  29. Constraint preserving mixers for the quantum approximate optimization algorithm. Algorithms, 15(6):202, 2022.
  30. J. Gambetta. IBM’s roadmap for scaling quantum technology. IBM Research Blog (September 2020), 2020.
  31. Quantum k-nearest neighbors classification algorithm based on mahalanobis distance. Frontiers in Physics, 10:1047466, 2022.
  32. Optimizing adiabatic quantum program compilation using a graph-theoretic framework. Quantum Information Processing, 17:1–26, 2018.
  33. L. K. Grover. A fast quantum mechanical algorithm for database search. In An. ACM symposium on Theory of computing, pages 212–219, 1996.
  34. L. Hales and S. Hallgren. An improved quantum fourier transform algorithm and applications. In An. Symposium on Foundations of Computer Science, pages 515–525, 2000.
  35. V. Harish and A. Kumar. A review on modeling and simulation of building energy systems. Renewable and Sustainable Energy Reviews, 56:1272–1292, 2016. ISSN 1364-0321.
  36. Quantum algorithm for linear systems of equations. Physical review letters, 103(15):150502, 2009.
  37. Formulating and solving routing problems on quantum computers. IEEE Trans. on Quantum Engineering, 2:1–17, 2021.
  38. Supervised learning with quantum-enhanced feature spaces. Nature, 567(7747):209–212, 2019.
  39. Zero-noise extrapolation for quantum-gate error mitigation with identity insertions. Physical Review A, 102(1):012426, 2020.
  40. Quantum programming languages. Nature Reviews Physics, 2(12):709–722, 2020.
  41. J. Hinze. Construction planning and scheduling, volume 228. 2004.
  42. D. Horn and A. Gottlieb. Algorithm for data clustering in pattern recognition problems based on quantum mechanics. Physical review letters, 88(1):018702, 2001.
  43. J. Humar. Dynamics of structures. 2012.
  44. D. Jaschke and S. Montangero. Is quantum computing green? an estimate for an energy-efficiency quantum advantage. Quantum Science and Technology, 8(2):025001, 2023.
  45. S. P. Jordan. Fast quantum algorithm for numerical gradient estimation. Physical review letters, 95(5), 2005.
  46. R. M. Karp. Reducibility among combinatorial problems. 2010.
  47. Evidence of kardar-parisi-zhang scaling on a digital quantum simulator. npj Quantum Information, 9(1):72, 2023.
  48. Quantum algorithms for deep convolutional neural networks. arXiv preprint arXiv:1911.01117, 2019.
  49. Evidence for the utility of quantum computing before fault tolerance. Nature, 618(7965):500–505, 2023.
  50. D. Kirk et al. NVIDIA CUDA software and GPU parallel computing architecture. In ISMM, volume 7, 2007.
  51. G. A. Kochenberger and F. Glover. A unified framework for modeling and solving combinatorial optimization problems: A tutorial. Multiscale optimization methods and applications, pages 101–124, 2006.
  52. L. Lamata. Basic protocols in quantum reinforcement learning with superconducting circuits. Scientific reports, 7(1):1609, 2017.
  53. A hidden markov model for route and destination prediction. In IEEE Int. Conf. on Intelligent Transportation Systems (ITSC), pages 1–6, 2017.
  54. Efficient quantum algorithm for dissipative nonlinear differential equations. Proc. of the National Academy of Sciences, 118(35), 2021a.
  55. A rigorous and robust quantum speed-up in supervised machine learning. Nature Physics, 17(9):1013–1017, 2021b.
  56. Quantum principal component analysis. Nature Physics, 10(9):631–633, 2014.
  57. L. Madden and A. Simonetto. Best approximate quantum compiling problems. ACM Trans. on Quantum Computing, 3(2):1–29, 2022.
  58. N. Mariella and S. Zhuk. A doubly stochastic matrices-based approach to optimal qubit routing. Quantum Information Processing, 22(7):264, 2023.
  59. L. Mei and Q. Wang. Structural optimization in civil engineering: a literature review. Buildings, 11(2):66, 2021.
  60. Quantum computation and quantum information. 2010.
  61. Large language models and knowledge graphs to optimize commercial real estate portfolio. In INFORMS An. Meeting, 2023.
  62. The prospects of quantum computing in computational molecular biology. WIREs Computational Molecular Science, 11(1):e1481, 2021.
  63. Quantum speedup for active learning agents. Physical Review X, 4(3):031002, 2014.
  64. Ai model factory: scaling ai for industry 4.0 applications. In AAAI, volume 37, pages 16467–16469, 2023.
  65. Forecasting gas usage for big buildings using generalized additive models and deep learning. In IEEE SMARTCOMP, pages 203–210, 2018.
  66. A variational eigenvalue solver on a photonic quantum processor. Nature communications, 5(1):4213, 2014.
  67. Demonstration of fault-tolerant universal quantum gate operations. Nature, 605(7911), 2022.
  68. J. Preskill. Quantum computing in the NISQ era and beyond. Quantum, 2:79, 2018.
  69. J. Preskill. Quantum computing 40 years later. In Feynman Lectures on Computation, pages 193–244. 2023.
  70. The quest for a quantum neural network. Quantum Information Processing, 13:2567–2586, 2014.
  71. R. Shaydulin and Y. Alexeev. Evaluating quantum approximate optimization algorithm: A case study. In Int. green and sustainable computing Conf. (IGSC), pages 1–6, 2019.
  72. P. W. Shor. Algorithms for quantum computation: discrete logarithms and factoring. In An. Symposium on Foundations of Computer Science, pages 124–134, 1994.
  73. Challenges in gpu-accelerated nonlinear dynamic analysis for structural systems. Journal of Structural Engineering, 149(3):04022253, 2023.
  74. Machine learning techniques for structural health monitoring. In 8th Eu. Workshop on Structural Health Monitoring, 2016.
  75. Machine learning applications for building structural design and performance assessment: State-of-the-art review. Journal of Building Engineering, 33:101816, 2021.
  76. P. Toth and D. Vigo. Vehicle routing: problems, methods, and applications. 2014.
  77. M. Treiber and A. Kesting. Traffic flow dynamics. Traffic Flow Dynamics: Data, Models and Simulation, Springer-Verlag Berlin Heidelberg, pages 983–1000, 2013.
  78. Probabilistic error cancellation with sparse pauli–lindblad models on noisy quantum processors. Nature Physics, pages 1–6, 2023.
  79. Opportunities and challenges of quantum computing for engineering optimization. Journal of Computing and Information Science in Engineering, 23(6), 2023.
  80. H. Wiseman and G. Milburn. Interpretation of quantum jump and diffusion processes illustrated on the bloch sphere. Physical Review A, 47(3):1652, 1993.
  81. Quantum shuttle: traffic navigation with quantum computing. In ACM SIGSOFT Int. Workshop on Architectures and Paradigms for Engineering Quantum Software, pages 22–30, 2020.
  82. The finite element method: its basis and fundamentals. 2005.

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