Fundamentals of Quantum Programming in IBM's Quantum Computers
This textbook introduces major topics that include quantum bits, superposition, entanglement, logic gates, quantum search algorithm, quantum Fourier transform, inverse quantum Fourier transform, Shor’s order-finding algorithm and phase estimation. Ev
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Weng-Long Chang Athanasios V. Vasilakos
Fundamentals of Quantum Programming in IBM’s Quantum Computers
Studies in Big Data Volume 81
Series Editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
The series “Studies in Big Data” (SBD) publishes new developments and advances in the various areas of Big Data- quickly and with a high quality. The intent is to cover the theory, research, development, and applications of Big Data, as embedded in the fields of engineering, computer science, physics, economics and life sciences. The books of the series refer to the analysis and understanding of large, complex, and/or distributed data sets generated from recent digital sources coming from sensors or other physical instruments as well as simulations, crowd sourcing, social networks or other internet transactions, such as emails or video click streams and other. The series contains monographs, lecture notes and edited volumes in Big Data spanning the areas of computational intelligence including neural networks, evolutionary computation, soft computing, fuzzy systems, as well as artificial intelligence, data mining, modern statistics and Operations research, as well as self-organizing systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output. Indexed by zbMATH. All books published in the series are submitted for consideration in Web of Science.
More information about this series at http://www.springer.com/series/11970
Weng-Long Chang Athanasios V. Vasilakos •
Fundamentals of Quantum Programming in IBM’s Quantum Computers
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Weng-Long Chang Department of Computer Science and Information Engineering National Kaohsiung University of Science and Technology Kaohsiung, Taiwan, Republic of China
Athanasios V. Vasilakos School of Electrical and Data Engineering University of Technology Sydney Sydney, Australia
ISSN 2197-6503 ISSN 2197-6511 (electronic) Studies in Big Data ISBN 978-3-030-63582-4 ISBN 978-3-030-63583-1 (eBook) https://doi.org/10.1007/978-3-030-63583-1 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and th
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