Introduction to Logical Entropy and its Relationship to Shannon Entropy
David Ellerman

TL;DR
This paper introduces logical entropy as a foundational measure of information based on distinctions, relates it to Shannon entropy through a transformation, and extends it to quantum systems, providing a new perspective on information theory.
Contribution
It formally defines logical entropy, establishes its relationship with Shannon entropy via a dit-bit transform, and extends the concept to quantum logical entropy.
Findings
Logical entropy measures distinctions in a set.
Shannon entropy can be derived from logical entropy through a nonlinear transform.
Quantum logical entropy relates to measurement probabilities in quantum states.
Abstract
We live in the information age. Claude Shannon, as the father of the information age, gave us a theory of communications that quantified an "amount of information," but, as he pointed out, "no concept of information itself was defined." Logical entropy provides that definition. Logical entropy is the natural measure of the notion of information based on distinctions, differences, distinguishability, and diversity. It is the (normalized) quantitative measure of the distinctions of a partition on a set--just as the Boole-Laplace logical probability is the normalized quantitative measure of the elements of a subset of a set. And partitions and subsets are mathematically dual concepts--so the logic of partitions is dual in that sense to the usual Boolean logic of subsets, and hence the name "logical entropy." The logical entropy of a partition has a simple interpretation as the probability…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Neural Networks and Applications
