Quantum Computing and Its Implications for Complex System Analysis
Article Sidebar
-
Algorithms Complex Systems Quantum Computing Quantum Simulations Superposition 6. Technology
Abstract
Quantum computing has emerged as a transformative technology capable of solving complex problems beyond the reach of classical computing. Its unique properties, such as superposition and entanglement, enable efficient processing of vast datasets, making it especially valuable for analyzing complex systems. This research aims to explore the implications of quantum computing for complex system analysis, particularly in fields such as physics, biology, and finance. The goal is to identify how quantum algorithms can enhance the understanding and modeling of intricate systems. A systematic literature review was conducted, examining recent advancements in quantum algorithms and their applications to complex system analysis. Comparative analyses were performed between classical and quantum computing approaches, focusing on specific case studies to illustrate the advantages of quantum solutions. The findings indicate that quantum computing significantly accelerates certain computations, leading to improved accuracy and efficiency in modeling complex systems. Case studies in quantum simulations of molecular interactions and financial modeling demonstrate substantial performance gains over classical methods. Quantum computing holds great promise for advancing the analysis of complex systems across various disciplines. Continued research and development in this area are essential to fully harness the capabilities of quantum technologies, ultimately leading to breakthroughs in understanding and solving complex problems.
Full text article
References
Ajagekar, A. (2022). Quantum computing and quantum artificial intelligence for renewable and sustainable energy: A emerging prospect towards climate neutrality. Renewable and Sustainable Energy Reviews, 165(Query date: 2024-11-10 06:44:25). https://doi.org/10.1016/j.rser.2022.112493
Ali, S. (2022). When software engineering meets quantum computing. Communications of the ACM, 65(4), 84–88. https://doi.org/10.1145/3512340
Allcock, D. T. C. (2021). Omg blueprint for trapped ion quantum computing with metastable states. Applied Physics Letters, 119(21). https://doi.org/10.1063/5.0069544
Alyami, H. (2021). The evaluation of software security through quantum computing techniques: A durability perspective. Applied Sciences (Switzerland), 11(24). https://doi.org/10.3390/app112411784
An, D. (2022). Quantum Linear System Solver Based on Time-optimal Adiabatic Quantum Computing and Quantum Approximate Optimization Algorithm. ACM Transactions on Quantum Computing, 3(2). https://doi.org/10.1145/3498331
Asthana, A. (2023). Quantum self-consistent equation-of-motion method for computing molecular excitation energies, ionization potentials, and electron affinities on a quantum computer. Chemical Science, 14(9), 2405–2418. https://doi.org/10.1039/d2sc05371c
Berke, C. (2022). Transmon platform for quantum computing challenged by chaotic fluctuations. Nature Communications, 13(1). https://doi.org/10.1038/s41467-022-29940-y
Bernal, D. E. (2022). Perspectives of quantum computing for chemical engineering. AIChE Journal, 68(6). https://doi.org/10.1002/aic.17651
Blunt, N. S. (2022). Perspective on the Current State-of-the-Art of Quantum Computing for Drug Discovery Applications. Journal of Chemical Theory and Computation, 18(12), 7001–7023. https://doi.org/10.1021/acs.jctc.2c00574
Bravyi, S. (2022). The future of quantum computing with superconducting qubits. Journal of Applied Physics, 132(16). https://doi.org/10.1063/5.0082975
Chamberland, C. (2022). Universal Quantum Computing with Twist-Free and Temporally Encoded Lattice Surgery. PRX Quantum, 3(1). https://doi.org/10.1103/PRXQuantum.3.010331
Choi, K. (2021). Rodeo Algorithm for Quantum Computing. Physical Review Letters, 127(4). https://doi.org/10.1103/PhysRevLett.127.040505
Dong, Y.-H., Peng, F.-L., & Guo, T.-F. (2021). Quantitative assessment method on urban vitality of metro-led underground space based on multi-source data: A case study of Shanghai Inner Ring area. Tunnelling and Underground Space Technology, 116, 104108. https://doi.org/10.1016/j.tust.2021.104108
Geyer, S. (2021). Self-aligned gates for scalable silicon quantum computing. Applied Physics Letters, 118(10). https://doi.org/10.1063/5.0036520
Ghosh, S. (2021). Realising and compressing quantum circuits with quantum reservoir computing. Communications Physics, 4(1). https://doi.org/10.1038/s42005-021-00606-3
Gill, S. S. (2024). Quantum and blockchain based Serverless edge computing: A vision, model, new trends and future directions. Internet Technology Letters, 7(1). https://doi.org/10.1002/itl2.275
Gonzalez-Zalba, M. F. (2021). Scaling silicon-based quantum computing using CMOS technology. Nature Electronics, 4(12), 872–884. https://doi.org/10.1038/s41928-021-00681-y
Govia, L. C. G. (2021). Quantum reservoir computing with a single nonlinear oscillator. Physical Review Research, 3(1). https://doi.org/10.1103/PhysRevResearch.3.013077
Han, J., Xu, K., Yan, Q., Sui, W., Zhang, H., Wang, S., Zhang, Z., Wei, Z., & Han, F. (2022). Qualitative and quantitative evaluation of Flos Puerariae by using chemical fingerprint in combination with chemometrics method. Journal of Pharmaceutical Analysis, 12(3), 489–499. https://doi.org/10.1016/j.jpha.2021.09.003
Hashim, A. (2021). Randomized Compiling for Scalable Quantum Computing on a Noisy Superconducting Quantum Processor. Physical Review X, 11(4). https://doi.org/10.1103/PhysRevX.11.041039
Hegade, N. N. (2021). Shortcuts to Adiabaticity in Digitized Adiabatic Quantum Computing. Physical Review Applied, 15(2). https://doi.org/10.1103/PhysRevApplied.15.024038
Hoffmann, A. (2022). Quantum materials for energy-efficient neuromorphic computing: Opportunities and challenges. APL Materials, 10(7). https://doi.org/10.1063/5.0094205
Ji, H., Qin, W., Yuan, Z., & Meng, F. (2021). Qualitative and quantitative recognition method of drug-producing chemicals based on SnO2 gas sensor with dynamic measurement and PCA weak separation. Sensors and Actuators B: Chemical, 348, 130698. https://doi.org/10.1016/j.snb.2021.130698
Jiulin, S., Quntao, Z., Xiaojin, G., & Jisheng, X. (2021). Quantitative Evaluation of Top Coal Caving Methods at the Working Face of Extra?Thick Coal Seams Based on the Random Medium Theory. Advances in Civil Engineering, 2021(1), 5528067. https://doi.org/10.1155/2021/5528067
Kavokin, A. (2022). Polariton condensates for classical and quantum computing. Nature Reviews Physics, 4(7), 435–451. https://doi.org/10.1038/s42254-022-00447-1
Khan, A. A. (2023). Software architecture for quantum computing systems—A systematic review. Journal of Systems and Software, 201(Query date: 2024-11-10 06:44:25). https://doi.org/10.1016/j.jss.2023.111682
Liu, L. (2021). QuCloud: A New Qubit Mapping Mechanism for Multi-programming Quantum Computing in Cloud Environment. Proceedings - International Symposium on High-Performance Computer Architecture, 2021(Query date: 2024-11-10 06:44:25), 167–178. https://doi.org/10.1109/HPCA51647.2021.00024
Mahendran, M., Lizotte, D., & Bauer, G. R. (2022). Quantitative methods for descriptive intersectional analysis with binary health outcomes. SSM - Population Health, 17, 101032. https://doi.org/10.1016/j.ssmph.2022.101032
Meurice, Y. (2022). Tensor lattice field theory for renormalization and quantum computing. Reviews of Modern Physics, 94(2). https://doi.org/10.1103/RevModPhys.94.025005
Moguel, E. (2022). Quantum service-oriented computing: Current landscape and challenges. Software Quality Journal, 30(4), 983–1002. https://doi.org/10.1007/s11219-022-09589-y
Morgado, M. (2021). Quantum simulation and computing with Rydberg-interacting qubits. AVS Quantum Science, 3(2). https://doi.org/10.1116/5.0036562
Mujal, P. (2021). Opportunities in Quantum Reservoir Computing and Extreme Learning Machines. Advanced Quantum Technologies, 4(8). https://doi.org/10.1002/qute.202100027
Outeiral, C. (2021). The prospects of quantum computing in computational molecular biology. Wiley Interdisciplinary Reviews: Computational Molecular Science, 11(1). https://doi.org/10.1002/wcms.1481
Phalak, K. (2021). Quantum PUF for Security and Trust in Quantum Computing. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 11(2), 333–342. https://doi.org/10.1109/JETCAS.2021.3077024
Rietsche, R. (2022). Quantum computing. Electronic Markets, 32(4), 2525–2536. https://doi.org/10.1007/s12525-022-00570-y
Smart, S. E. (2021). Quantum Solver of Contracted Eigenvalue Equations for Scalable Molecular Simulations on Quantum Computing Devices. Physical Review Letters, 126(7). https://doi.org/10.1103/PhysRevLett.126.070504
Stetcu, I. (2022). Variational approaches to constructing the many-body nuclear ground state for quantum computing. Physical Review C, 105(6). https://doi.org/10.1103/PhysRevC.105.064308
Swarna, S. R. (2021). Parkinson’s disease prediction using adaptive quantum computing. Proceedings of the 3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021, Query date: 2024-11-10 06:44:25, 1396–1401. https://doi.org/10.1109/ICICV50876.2021.9388628
Tan, B. (2021). Optimality Study of Existing Quantum Computing Layout Synthesis Tools. IEEE Transactions on Computers, 70(9), 1363–1373. https://doi.org/10.1109/TC.2020.3009140
Yang, C. H. H. (2022). WHEN BERT MEETS QUANTUM TEMPORAL CONVOLUTION LEARNING FOR TEXT CLASSIFICATION IN HETEROGENEOUS COMPUTING. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2022(Query date: 2024-11-10 06:44:25), 8602–8606. https://doi.org/10.1109/ICASSP43922.2022.9746412
Authors
Copyright (c) 2024 Kiran Iqbal, Omar Ahmad, Arnes Yuli Vandika
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
