The Role of Quantum Computing in Advancing Cross-disciplinary Informatics: A Theoretical Framework
Downloads
Background. Quantum computing is a technology that has great potential to revolutionize various scientific disciplines with its extraordinary computing capabilities. Multiple fields, such as cryptography, optimization, and complex simulation, have begun to explore quantum computing applications. However, a deep understanding of how these technologies can be used effectively in cross-disciplinary informatics still needs to be improved.
Purpose. This research aims to develop a theoretical framework that explains the role of quantum computing in advancing interdisciplinary informatics. The main focus is to identify potential applications and challenges faced in applying this technology in various fields and evaluate its impact on the development of science and technology.
Method. This research uses a qualitative approach, a literature review, and theoretical analysis methods. Data were collected from various academic sources, including journals, books, and conferences relevant to quantum computing and interdisciplinary informatics. Analysis was conducted to identify patterns, themes, and relationships between quantum computing and cross-disciplinary applications.
Results. The research results show that quantum computing has the potential to solve complex computational problems in various fields, including cryptography, optimization, and physical simulation. Quantum computing can provide more efficient and faster solutions than classical computing. However, the research also identified several significant challenges, such as the need for quantum-specific algorithms and sophisticated technological infrastructure.
Conclusion. Quantum computing is essential in advancing interdisciplinary informatics by providing superior computing capabilities. The theoretical framework developed in this research can be used to further study and develop quantum computing applications in various fields. The identified challenges require special attention to ensure effective and efficient implementation of this technology.
Abadi, E. (2019). DukeSim: A realistic, rapid, and scanner-specific simulation framework in computed tomography. IEEE Transactions on Medical Imaging, 38(6), 1457–1465. https://doi.org/10.1109/TMI.2018.2886530
Abbas, A. (2020). On quantum ensembles of quantum classifiers. Quantum Machine Intelligence, 2(1). https://doi.org/10.1007/s42484-020-00018-6
Aifer, M. (2022). From quantum speed limits to energy-efficient quantum gates. New Journal of Physics, 24(5). https://doi.org/10.1088/1367-2630/ac6821
Anand, A. (2022). A quantum computing view on unitary coupled cluster theory. Chemical Society reviews, 51(5), 1659–1684. https://doi.org/10.1039/d1cs00932j
Budinski, L. (2021). Quantum algorithm for the advection–diffusion equation simulated with the lattice Boltzmann method. Quantum Information Processing, 20(2). https://doi.org/10.1007/s11128-021-02996-3
Cai, R. (2019). A stochastic-computing based deep learning framework using adiabatic quantum-flux-parametron superconducting technology. Proceedings - International Symposium on Computer Architecture, Query date: 2024-07-01 09:36:11, 567–578. https://doi.org/10.1145/3307650.3322270
Chakraborty, S. (2020). A hybrid quantum feature selection algorithm using a quantum inspired graph theoretic approach. Applied Intelligence, 50(6), 1775–1793. https://doi.org/10.1007/s10489-019-01604-3
Chehimi, M. (2022). QUANTUM FEDERATED LEARNING WITH QUANTUM DATA. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2022(Query date: 2024-07-01 09:36:11), 8617–8621. https://doi.org/10.1109/ICASSP43922.2022.9746622
Chourasia, S., Pandey, S. M., & Keshri, A. K. (2023). Prospects and Challenges with Legal Informatics and Legal Metrology Framework in the Context of Industry 6.0. MAPAN, 38(4), 1027–1052. https://doi.org/10.1007/s12647-023-00664-8
Coste, N. (2023). High-rate entanglement between a semiconductor spin and indistinguishable photons. Nature Photonics, 17(7), 582–587. https://doi.org/10.1038/s41566-023-01186-0
Das, P. (2021). JigSaw: Boosting fidelity of NISQ programs via measurement subsetting. Proceedings of the Annual International Symposium on Microarchitecture, MICRO, Query date: 2024-07-01 09:36:11, 937–949. https://doi.org/10.1145/3466752.3480044
Deng, W. (2021). Quantum differential evolution with cooperative coevolution framework and hybrid mutation strategy for large scale optimization. Knowledge-Based Systems, 224(Query date: 2024-07-01 09:36:11). https://doi.org/10.1016/j.knosys.2021.107080
Deshpande, N. (2023). Empirical Analysis of the Emerging Trends in the Photovoltaic Technologies of Condensed Matter Physics. Journal of Nano- and Electronic Physics, 15(4). https://doi.org/10.21272/jnep.15(4).04009
Dove, J. (2021). The asymmetry of antimatter in the proton. Nature, 590(7847), 561–565. https://doi.org/10.1038/s41586-021-03282-z
Elijah, O. (2021). A Survey on Industry 4.0 for the Oil and Gas Industry: Upstream Sector. IEEE Access, 9(Query date: 2024-07-01 09:36:11), 144438–144468. https://doi.org/10.1109/ACCESS.2021.3121302
Elliott, J. D. (2019). Koopmans Meets Bethe-Salpeter: Excitonic Optical Spectra without GW. Journal of Chemical Theory and Computation, 15(6), 3710–3720. https://doi.org/10.1021/acs.jctc.8b01271
Fedorov, A. K. (2019). Quantum technologies in Russia. Quantum Science and Technology, 4(4). https://doi.org/10.1088/2058-9565/ab4472
Fluck, A. E. (2023). Arguing for a Quantum Computing Curriculum: Lessons from Australian Schools. Dalam T. Keane, C. Lewin, T. Brinda, & R. Bottino (Ed.), Towards a Collaborative Society Through Creative Learning (Vol. 685, hlm. 137–148). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-43393-1_14
Gely, M. F. (2020). QuCAT: Quantum circuit analyzer tool in Python. New Journal of Physics, 22(1). https://doi.org/10.1088/1367-2630/ab60f6
Ghosh, S. (2021). Reconstructing Quantum States with Quantum Reservoir Networks. IEEE Transactions on Neural Networks and Learning Systems, 32(7), 3148–3155. https://doi.org/10.1109/TNNLS.2020.3009716
Gilyén, A. (2019). Optimizing quantum optimization algorithms via faster quantum gradient computation. Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms, Query date: 2024-07-01 09:36:11, 1425–1444. https://doi.org/10.1137/1.9781611975482.87
Giurgica-Tiron, T. (2020). Digital zero noise extrapolation for quantum error mitigation. Proceedings - IEEE International Conference on Quantum Computing and Engineering, QCE 2020, Query date: 2024-07-01 09:36:11, 306–316. https://doi.org/10.1109/QCE49297.2020.00045
Gran, U. (2019). Exotic holographic dispersion. Journal of High Energy Physics, 2019(2). https://doi.org/10.1007/JHEP02(2019)032
Guo, D. (2020). Comprehensive high-speed reconciliation for continuous-variable quantum key distribution. Quantum Information Processing, 19(9). https://doi.org/10.1007/s11128-020-02832-0
Gyongyosi, L. (2019). A Survey on quantum computing technology. Computer Science Review, 31(Query date: 2024-07-01 09:36:11), 51–71. https://doi.org/10.1016/j.cosrev.2018.11.002
Huang, Y. (2020). Quantum algorithm for solving hyperelliptic curve discrete logarithm problem. Quantum Information Processing, 19(2). https://doi.org/10.1007/s11128-019-2562-5
Ibe, Y. (2022). Calculating transition amplitudes by variational quantum deflation. Physical Review Research, 4(1). https://doi.org/10.1103/PhysRevResearch.4.013173
Khairy, S. (2020). Learning to optimize variational quantum vircuits to solve combinatorial problems. AAAI 2020 - 34th AAAI Conference on Artificial Intelligence, Query date: 2024-07-01 09:36:11, 2367–2375.
Kumar, S. J. N. (2020). An effective non-commutative encryption approach with optimized genetic algorithm for ensuring data protection in cloud computing. CMES - Computer Modeling in Engineering and Sciences, 125(2), 671–697. https://doi.org/10.32604/cmes.2020.09361
Lostaglio, M. (2021). Error Mitigation and Quantum-Assisted Simulation in the Error Corrected Regime. Physical Review Letters, 127(20). https://doi.org/10.1103/PhysRevLett.127.200506
Lubinski, T. (2023). Application-Oriented Performance Benchmarks for Quantum Computing. IEEE Transactions on Quantum Engineering, 4(Query date: 2024-07-01 09:36:11). https://doi.org/10.1109/TQE.2023.3253761
Mannone, M., & Compagno, G. (2022). Characterisation of the Degree of Musical Non-Markovianity. Journal of Creative Music Systems, 6(1). https://doi.org/10.5920/jcms.975
Mao, Q. (2023). Classical and reactive molecular dynamics: Principles and applications in combustion and energy systems. Progress in Energy and Combustion Science, 97(Query date: 2024-07-01 09:36:11). https://doi.org/10.1016/j.pecs.2023.101084
Motta, M. (2022). Emerging quantum computing algorithms for quantum chemistry. Wiley Interdisciplinary Reviews: Computational Molecular Science, 12(3). https://doi.org/10.1002/wcms.1580
Müller, C. (2019). Towards understanding two-level-systems in amorphous solids: Insights from quantum circuits. Reports on Progress in Physics, 82(12). https://doi.org/10.1088/1361-6633/ab3a7e
Persson, M. (2020). Detective quantum efficiency of photon-counting CdTe and Si detectors for computed tomography: A simulation study. Journal of Medical Imaging, 7(4). https://doi.org/10.1117/1.JMI.7.4.043501
Pismak, Yu. M. (2023). Entangled states in a simple model of quantum electrodynamics. Theoretical and Mathematical Physics, 217(1), 1487–1494. https://doi.org/10.1134/S0040577923100057
Turukmane, A. V., & Khekare, G. (2023). Cyber Quantum Computing (Security) Using Rectified Probabilistic Packet Mark for Big Data. Dalam R. Rawat, R. K. Chakrawarti, S. K. Sarangi, J. Patel, V. Bhardwaj, A. Rawat, & H. Rawat (Ed.), Quantum Computing in Cybersecurity (1 ed., hlm. 1–15). Wiley. https://doi.org/10.1002/9781394167401.ch1
Yuan, X. (2021). Quantum Simulation with Hybrid Tensor Networks. Physical Review Letters, 127(4). https://doi.org/10.1103/PhysRevLett.127.040501
Copyright (c) 2024 Erna Hudianti Pujiarini, Ira Zulfa
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
