Abstract
Quantum random walks have caught the attention of quantum information theorists in recent years. Classical walks have been used to solve or design several highly efficient randomized algorithms, and they are also used in many quantum algorithms, but quantum walks provide an exponential speedup over classical walks since they can solve some oracle problems. For several practical problems, such as the element distinctness problem, the triangle finding problem, and evaluating NAND trees, quantum walks provide polynomial speedups over classical algorithms. In this paper, we propose a new quantum random walk representation based on the drunken walk and the classical random walk. This current portrayal would make it easier for people to comprehend the potential of quantum computing. It will also be shown how to solve a large deviation analysis through quantum walks. This paper includes an explanation of how an inebriated person might locate his friend in a bar after leaving the restroom, as well as a comparison of quantum walks and classical walks.
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Biswas, S., Goswami, R.S. (2022). Understanding Quantum Computing Through Drunken Walks. In: Gupta, D., Goswami, R.S., Banerjee, S., Tanveer, M., Pachori, R.B. (eds) Pattern Recognition and Data Analysis with Applications. Lecture Notes in Electrical Engineering, vol 888. Springer, Singapore. https://doi.org/10.1007/978-981-19-1520-8_52
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DOI: https://doi.org/10.1007/978-981-19-1520-8_52
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