concept

entropy

Entropy is a measure of disorder or uncertainty in a system, often related to energy dispersal and information.
19 related articles · 6 formulas · 6 who worked here

Description

Entropy is a measure of disorder. Imagine a child's room: when toys are neatly arranged on shelves, entropy is low. But after a fun game, everything is scattered, and you can only restore order by making an effort. In the Universe, entropy always increases.

History

Rudolf Clausius introduced the term "entropy" in 1865, formulating the second law. Ludwig Boltzmann in the 1870s linked entropy with probability, proposing the formula S = k log W. The thought experiment of "Maxwell's demon" (1867) stimulated research into the connection between entropy and information, culminating in the works of Leo Szilard (1929) and Shannon (1948). In the 1970s, Jacob Bekenstein suggested that black holes have entropy proportional to the horizon area, and Stephen Hawking in 1974 confirmed this by discovering Hawking radiation and derived the exact formula.

How it works

According to the second law of thermodynamics, the total entropy of a closed system grows, approaching a maximum. This is due to the statistical nature of molecular motion: the system evolves from less probable states to more probable ones. Entropy is related to free energy: F = U - TS. In quantum theory, von Neumann entropy S(ρ) = -Tr(ρ ln ρ) generalizes the classical concept. For black holes, the Bekenstein-Hawking entropy S_BH = (k A)/(4 l_P²), where A is the horizon area, and l_P is the Planck length. This formula hints at the holographic principle.

Fun fact

If you shuffle a deck of cards, you will never return it to its original order just by shaking. That's entropy in action - disorder wins.

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Formulas

$$C(\psi) = 2|\alpha\delta - \beta\gamma|$$
Concurrence measures entanglement from 0 to 1.
What is in the formula
ψvariabletwo-qubit pure state — quantum state
αvariableamplitude of |00> component — probability amplitude
βvariableamplitude of |01> component — probability amplitude
γvariableamplitude of |10> component — probability amplitude
δvariableamplitude of |11> component — probability amplitude
δ(|ψ\rangle)\equiv2|αδ-βγ| · 2505.00873v1
$$S = k_B \ln \Omega$$
Entropy equals Boltzmann constant times log of states.
What is in the formula
Svariableentropy of the system — entropy joule per kelvin
Ωvariablenumber of microstates corresponding to the macrostate — number of microstates
k_Bconstantrelates temperature to energy per degree of freedom — boltzmann constant = 1.380649e-23 joule per kelvin
lnoperatornatural logarithm — natural logarithm
s=k_b\lnω · 2505.02040v2
s=k\logw · 2604.06450
$$S \leq k \ln W$$
Entropy is bounded by the logarithm of the number of states.
What is in the formula
SvariableEntropy — entropy joule per kelvin
WvariableNumber of microstates — number of states
kconstantRelates temperature to energy in statistical mechanics — boltzmann constant = 1.380649e-23 joule per kelvin
\leqoperatorIndicates an upper bound — less than or equal
\lnoperatorComputes the logarithm to base e — natural logarithm
s\leq\ln2 · 2510.27511
$$\eta = \frac{n_B}{s}$$
Ratio of baryon number density to entropy density.
What is in the formula
\etavariableBaryon to entropy ratio — baryon to entropy ratio
n_BvariableBaryon number density — baryon number density per cubic metre
svariableEntropy density — entropy density joule per cubic metre per kelvin
\eta=\frac{n_b}{s} · 2605.02008
$$S = -\sum_i p_i \ln p_i$$
Measure of uncertainty in a probability distribution.
What is in the formula
SvariableShannon entropy — entropy
p_ivariableProbability of the i-th outcome — probability
\sum_ioperatorSum over all possible outcomes i — summation
\lnoperatorNatural logarithm (base e) — natural logarithm
s=-\sum_ip_i\lnp_i · 2606.08686
h=-\sum_{i}p_i\log_2p_i · 2607.01833
$$\mathcal{L} = -\sum_i y_i \log(p_i)$$
Measures difference between predicted and true probability distributions.
What is in the formula
\mathcal{L}variableCross-entropy loss value — loss
y_ivariableTrue probability (or one-hot label) for class i — probability
p_ivariablePredicted probability for class i — probability
\sum_ioperatorAdds the terms for each class — summation
\logoperatorComputes the logarithm to base e — natural logarithm
\mathcal{l}=-\frac{1}{n}\sum_{i=1}^n[y_i\log(p_i)+(1-y_i)\log(1-p_i)]w_i · 2606.28603

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