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QUANTUM ALGORITHMS: UNLEASHING THE POWER OF QUANTUM COMPUTING

Mr. Shashikant Sharma, Dr. Sanjeev Solanki, Dr. Aliyu Yahya

First Published April 13,2024

Authors
  1. Mr. Shashikant Sharma
  2. Dr. Sanjeev Solanki
  3. Dr. Aliyu Yahya
Affiliation
  • Assistant Professor, Poddar Management, Technical Campus, Jaipur
  • Professor, Tula
  • Assistant Professor, Adamwa State Polytechnic, Adamwa, Nigeria
Abstract
Quantum computing, based on the fundamental principles of quantum mechanics, has become a breakthrough that has the potential to revolutionize computing. At the forefront of this quantum revolution are quantum algorithms that use the origin of qubits to solve computational problems much faster than classical algorithms. This article provides a review of quantum algorithms to introduce their principles, examine famous examples such as the Grover and Shor algorithms, and delve into their applications, including quantum machine learning.
Exploring the fundamentals of quantum computing begins with an overview that highlights the unique features of quantum computing compared to classical computing. We then examine several different algorithmic paradigms to highlight the unique advantages that quantum parallelism and interference bring to computing. The best knowledge of quantum algorithms focuses on Grover's blind search, Shor's efficient factorization algorithm, and the quantum phase approach for solving quantum chemistry and eigenvalue problems.
As well as celebrating the achievements of quantum algorithms, this research also looks at the challenges and limitations that hinder their widespread use. Topics such as decoherence, error correction, and finding errors in quantum computing are discussed in the context of overcoming obstacles to the use of practical quantum algorithms. In addition, the latest developments are clarified by showing the successes of the experiments and their implications for the future.
As we stand at the forefront of a new era in computing, this article not only provides a snapshot of the current state of quantum algorithms but also speculates on future directions. The potential applications of quantum algorithms in many fields and the ongoing quest to overcome current limitations offer exciting opportunities for further research. This research aims to contribute to the ongoing debate about quantum algorithms, gaining a deeper understanding of their impact on the future of computing.
Keywords

Quantum Computing, Qubit, Entanglement, Shor, QML, QSVM, QNN, QPCA

References
  1. Nielsen, M. A., & Chuang, I. L. (2010). Quantum Computation and Quantum Information. Cambridge University Press. Aaronson, S., & Arkhipov, A. (2011). The Computational Complexity of Linear Optics. Theory of Computing, 9(4), 143–252. Lloyd, S., Mohseni, M., & Rebentrost, P. (2014). Quantum Algorithms for Supervised and Unsupervised Machine Learning. arXiv preprint arXiv:1307.0411. Coppersmith, D. (1994). An approximate Fourier transform useful in quantum factoring. IBM Research Report RC19642. Farhi, E., Goldstone, J., & Gutmann, S. (2000). A quantum approximate optimization algorithm. arXiv preprint quant-ph/0001106. Arute, F., Arya, K., Babbush, R., Bacon, D., Bardin, J. C., Barends, R., ... & Boixo, S. (2019). Quantum supremacy using a programmable superconducting processor. Nature, 574(7779), 505-510. Farrow, T., Matsuura, A. Y., Martínez, E. A., & Aspuru-Guzik, A. (2020). Quantum algorithms for quantum chemistry and quantum materials science: Status, challenges and opportunities. Quantum Science and Technology, 5(3), 034014. Quantum Algorithms and Applications. (2023). [Online] Available at: [URL] [9] Aharonov, D., & Ben-Or, M. (2008). Fault-tolerant quantum computation with constant error rate. SIAM Journal on Computing, 38(3), 1207-1282. Gidney, C., & Campbell, E. (2020). Simulating low-depth circuits on stabilizer-based quantum computers. Quantum, 4, 280. Childs, A. M., & Van Dam, W. (2010). Quantum algorithms for fixed qubit architectures. Quantum Information & Computation, 10(1-2), 5-40.
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