Book contents
- Classical and Quantum Information Theory
- Reviews
- Classical and Quantum Information Theory
- Copyright page
- Contents
- Preface
- Acknowledgments
- Notations
- Acronyms
- Part I Classical Information
- Part II Quantum Information
- 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
- Part V Bipartite Quantum Information
- Part VI Multipartite Classical and Quantum Information
- Postface
- Index
- References
9 - Dynamic Classical Information
from Part III - Dynamic Information
Published online by Cambridge University Press: aN Invalid Date NaN
Book contents
- Classical and Quantum Information Theory
- Reviews
- Classical and Quantum Information Theory
- Copyright page
- Contents
- Preface
- Acknowledgments
- Notations
- Acronyms
- Part I Classical Information
- Part II Quantum Information
- 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
- Part V Bipartite Quantum Information
- Part VI Multipartite Classical and Quantum Information
- Postface
- Index
- References
Summary
This chapter explores how state evolution impacts uncertainty in classical systems using majorization and related principles.
Information
- Type
- Chapter
- Information
- Classical and Quantum Information TheoryUncertainty, Information, and Correlation, pp. 371 - 397Publisher: Cambridge University PressPrint publication year: 2026
References
Nielsen, M. and Vidal, G., “Majorization and the interconversion of bipartite states,” Quantum Information & Computation, vol. 1, no. 1, pp. 76–93, 2001.CrossRefGoogle Scholar
Olkin, I. and Marshall, A., Inequalities: Theory of Majorization and Its Applications. New York: Academic Press, 2016.Google Scholar
Boyd, S. and Vandenberghe, L., Convex Optimization. Cambridge: Cambridge University Press, 2004.CrossRefGoogle Scholar
Polyanskiy, Y. and Wu, Y., Information Theory: From Coding to Learning. Cambridge: Cambridge University Press, 2024.CrossRefGoogle Scholar
Gour, G. and Tomamichel, M., “Entropy and relative entropy from information-theoretic principles,” IEEE Transactions on Information Theory, vol. 67, no. 10, pp. 6313–6327, 2021.CrossRefGoogle Scholar
Brandsen, S., Geng, I. J., and Gour, G., “What is entropy? A perspective from games of chance,” Physical Review E, vol. 105, no. 2, art. 024117, 2022.CrossRefGoogle ScholarPubMed
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