Abstract
In this chapter, we will study the quantum Fourier transform and its application in different quantum algorithms. Problems such as factoring an integer into prime numbers or period finding are computationally intractable problems for a classical computer because of the exponentially large number of operations involved. Integer factoring and period finding can be efficiently solved using the quantum phase estimation algorithm that is heavily based on the quantum Fourier transform. Alternately, since quantum phase estimation aims to find the eigenvalue corresponding to an eigenvector of a unitary operator, it is backbone of important algorithms in optimization such as the HHL algorithm (named for Hassim, Harrow, and Lloyd), which serves as the matrix inversion routine in quantum computing. We start this chapter by revising our concepts of the Fourier transform and its discrete counterpart, the discrete Fourier transform, and then move on to the exciting domain of the quantum Fourier transform and the quantum phase estimation algorithm. We follow this up with a discussion and implementation of the few quantum Fourier transform–related algorithms such as factoring a number and period finding. At the end of the chapter, we briefly introduce the basics of group theory with an attempt to explain the hidden subgroup problem and how it relates to several of the Fourier transform–based algorithms.
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© 2021 Santanu Pattanayak
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Pattanayak, S. (2021). Quantum Fourier Transform and Related Algorithms. In: Quantum Machine Learning with Python. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-6522-2_4
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DOI: https://doi.org/10.1007/978-1-4842-6522-2_4
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Publisher Name: Apress, Berkeley, CA
Print ISBN: 978-1-4842-6521-5
Online ISBN: 978-1-4842-6522-2
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