Classical and Quantum Information Theory

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.

• 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.

'Professor Simeone presents a comprehensive synthesis of classical and quantum information theory, bridging foundational principles with modern perspectives. This book provides students and researchers with the essential foundations for exploring the central role of information theory in signal processing and communications, as well as its emerging quantum dimensions. The presentation is very well organized pedagogically, with clear learning objectives and exposition of needed background materials throughout.' Vincent Poor, Princeton University