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Discover the foundations of classical and quantum information theory in the digital age with this modern introductory textbook. Familiarise yourself with core topics such as uncertainty, correlation, and entanglement before exploring modern techniques and concepts including tensor networks, quantum circuits and quantum discord. Deepen your understanding and extend your skills with over 250 thought-provoking end-of-chapter problems, with solutions for instructors, and explore curated further reading. Understand how abstract concepts connect to real-world scenarios with over 400 examples, including numerical and conceptual illustrations, and emphasising practical applications. Build confidence as chapters progressively increase in complexity, alternating between classic and quantum systems. This is the ideal textbook for senior undergraduate and graduate students in electrical engineering, computer science, and applied mathematics, looking to master the essentials of contemporary information theory.
List of contents
Preface; References; Acknowledgements; Notations; Acronyms; Part I. Classical Information: 1. Uncertainty, information, and entropy; 2. Information quantification by asking, compressing, or sampling: Shannon entropy; 3. Information quantification by predicting or guessing: Tsallis entropy and Rényi entropy; 4. Relative entropy; Part II. Quantum Information: 5. From classical to quantum information; 6. Quantum uncertainty: measured entropy, coherence, and the uncertainty principle; 7. Classical and quantum uncertainty: mixed states and quantum entropy; 8. Quantum relative entropy; Part III. Dynamic Information: 9. Dynamic classical information; 10. Quantum dynamic information in closed systems; 11. Quantum dynamic information in open systems; Part IV. Bipartite Classical Information: 12. Bipartite classical information as correlation; 13. Bipartite classical information via residual uncertainty; Part V. Bipartite Quantum Information: 14. Bipartite classical and quantum Information for pure states; 15. Bipartite dynamic classical and quantum information for pure states; 16. Bipartite quantum and classical information for mixed states; Part VI. Multipartite Classical and Quantum Information: 17. A brief introduction to tensor networks; 18. Multipartite classical information: fragmentation, scale, and strength; 19. Multipartite classical information: structure; 20. Multipartite quantum information: fragmentation, scale, and strength; 21. Multipartite quantum information: structure; Index.
About the author
Osvaldo Simeone is a Professor of Information Engineering at King's College London, where he co-directs the Centre for Intelligent Information Processing Systems. He is the author of Machine Learning for Engineers (2022) and of several monographs. He is the recipient of a number of best paper awards, and he is a Fellow of the IEEE, EPSRC, and IET.
