ADVANCED APPLICATIONS OF QUANTUM COMPUTING IN MODERN SYSTEMS: PRINCIPLES, MATHEMATICAL FOUNDATIONS, AND FUTURE DIRECTIONS
DOI:
https://doi.org/10.53555/h2qph672Keywords:
Quantum Computing, Quantum Algorithms, Entanglement, Quantum Circuits, Quantum Machine LearningAbstract
Quantum computing leverages the fundamental principles of quantum mechanics to address problems that are computationally infeasible for classical systems. This paper presents a comprehensive study of quantum computing with a focus on its mathematical foundations—superposition, entanglement, quantum gates, and quantum circuits. We explore the im- plementation and practical significance of core quantum algorithms, in- cluding Shor’s algorithm for integer factorization and Grover’s algorithm for unstructured search. Emerging domains such as quantum machine learning, quantum optimization, and quantum chemistry are also exam- ined. Mathematical examples are used to illustrate the operational mech- anisms of quantum systems in real-world applications. We further ana- lyze key challenges, including scalability, quantum error correction, and hardware constraints. The paper concludes with a discussion on future research directions and the evolving role of quantum computing in modern computational paradigms.
References
[1] Jacob Biamonte, Peter Wittek, Nicola Pancotti, R. D. Somma, M. Kieferov´a, S. Yang, B. Matus, H. Gehring, J. T. Seeley, and P. J. Love. Quantum machine learning. Nature, 549:195–202, 2017.
[2] Boris B. Blinov, J. Chen, M. Rowe, S. Seidelin, and D. Wineland. Quantum computing with trapped ions. Nature, 428:153–157, 2004.
[3] Albert Einstein, Boris Podolsky, and Nathan Rosen. Can quantum- mechanical description of physical reality be considered complete? Physical Review, 47(10):777–780, 1935.
[4] Albert Einstein, Boris Podolsky, and Nathan Rosen. Quantum mechanics and reality. Physical Review, 47(10):777–780, 1935.
[5] Edward Farhi, Jeff Goldstone, and Sam Gutmann. Quantum computation by adiabatic evolution. Quantum Information and Computation, 4(7):417– 427, 2014.
[6] Edward Farhi and Hartmut Neven. Classification with quantum neural networks on near term processors. arXiv preprint arXiv:1802.06002, 2018.
[7] Austin G. Fowler, Simon J. Devitt, and Lloyd C. L. Hollenberg. Surface codes: Towards practical large-scale quantum computation. Physical Re- view A, 82(5):050304, 2012.
[8] Lov K. Grover. A fast quantum mechanical algorithm for database search. In Proceedings of the 28th Annual ACM Symposium on Theory of Comput- ing, pages 212–219, 1996.
[9] Alexander Kitaev. Fault-tolerant quantum computation by anyons. Annals of Physics, 303(1):2–30, 2003.
[10] Seth Lloyd, Masoud Mohseni, and Patrick Rebentrost. Quantum principal component analysis. Nature Physics, 10:631–633, 2014.
[11] Chris Neill, E. Lucero, and P. et al. Roushan. Superconducting quantum circuits at the surface code threshold. Science, 360(6385):195–199, 2018.
[12] Michael A. Nielsen and Isaac L. Chuang. Quantum Computation and Quan- tum Information. Cambridge University Press, 2010.
[13] L. O’Brien, A. Furusawa, and J. Vucovic. Photonic quantum technologies. Nature Photonics, 3:687–695, 2009.
[14] Alberto Peruzzo, Jarrod R. McClean, and Peter et al. Shadbolt. A varia- tional eigenvalue solver on a quantum processor. Nature Communications, 5:4213, 2014.
[15] John Preskill. Quantum computing in the nisq era and beyond. Quantum, 2:79, 2018.
[16] Patrick Rebentrost, Masoud Mohseni, and Seth Lloyd. Quantum sup- port vector machine for big data classification. Physical Review Letters, 113(13):130503, 2014.
[17] Maria Schuld, Nathan Killoran, Baiying Zeng, Dominic Markham, and Dieter Suter. Introduction to quantum machine learning. arXiv preprint arXiv:1501.01756, 2015.
[18] Maria Schuld, Ilya Sinayskiy, and Francesco Petruccione. The quest for a quantum neural network. Quantum Information Processing, 13(3):1045– 1065, 2014.
[19] Peter W. Shor. Algorithms for quantum computation: discrete logarithms and factoring. In Proceedings of the 35th Annual Symposium on Founda- tions of Computer Science, pages 124–134, 1994.
[20] Peter W. Shor. Scheme for reducing decoherence in quantum computer memory. Physical Review A, 52(4):R2493–R2496, 1995.
[21] Nathaniel Wiebe, Daniel Braun, and Seth Lloyd. Quantum principal com- ponent analysis. Physical Review Letters, 109(5):050505, 2012.
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