LowEntropy

Low entropy describes a condition in which a system exhibits less disorder or lower uncertainty than a reference state. The term is used in physics and information theory, with related but distinct meanings in each field.

In thermodynamics, entropy is a measure of the number of microscopic configurations compatible with a macroscopic state. A low-entropy state is highly ordered, with relatively few accessible microstates. Examples include crystalline solids at low temperatures or other highly structured arrangements. Achieving or maintaining low entropy typically requires energy exchange with the environment; while entropy tends to increase in isolated systems (the second law), local reductions are possible if compensated by greater entropy production elsewhere, for instance by removing heat or performing work.

In information theory, low entropy pertains to the average unpredictability of outcomes produced by a source. A source with low Shannon entropy yields highly predictable data (for example, a binary source that outputs 0 with probability near 1). Such data are highly compressible, because redundancy reduces effective information content. Conversely, high-entropy data are more random and less compressible.

In practice, the term is used to describe transient or constrained states where order or predictability is unusually high relative to a reference. It also appears in discussions of algorithmic information, thermodynamic cycles, and data compression. Important caveats include that entropy is context-dependent and that the second law constrains decreases in total entropy to nonisolated systems. See also entropy, second law of thermodynamics, Shannon entropy, data compression, and order and disorder.