theorem

Bekenstein bound

The Bekenstein bound is an upper limit on the entropy or information that can be contained in a finite region of space.
21 related articles · 6 formulas · 1 who worked here

Description

Where did the idea of a connection between information and area come from? In the 1970s, Stephen Hawking and Jacob Bekenstein pondered: if you throw a cup of hot tea into a black hole, the tea's entropy would disappear, and the total entropy of the Universe would seemingly decrease—violating the second law of thermodynamics. The solution came from calculations: a black hole's entropy must be proportional to the area of its event horizon (the surface from which nothing can escape). Bekenstein went further: he hypothesized that any physical system contained within a sphere of radius R with energy E cannot have an entropy greater than that of a similar sphere that had collapsed into a black hole. Thus the Bekenstein bound was born—a bridge between gravity, thermodynamics, and information theory. This bound is like a rule: no matter how many books you place on a shelf, their total ‘story capacity’ cannot exceed the throughput of the door to the room. The door here is the surface area.

History

The theorem was proposed by Jacob Bekenstein in 1981 (Physical Review D, 23, 287). By then Bekenstein was already known as the originator of black hole entropy (1972). The initial impetus came from a thought experiment: if you lower an entropy-bearing object into a black hole, its horizon must increase just enough to compensate for the loss of entropy from the outside world; otherwise, the second law of thermodynamics would be violated. Generalizing, Bekenstein derived a limit for any system, not necessarily one collapsing into a hole. Later the bound was corroborated within the framework of quantum information theory and the holographic principle of 't Hooft and Susskind.

How it works

The Bekenstein bound is actively used in black hole physics, quantum gravity, and quantum information theory. For black holes it is saturated—their entropy equals exactly A/(4ℓ_P²), where A is the horizon area, ℓ_P the Planck length. For ordinary macroscopic bodies the bound is astronomically large and not reached; for example, for a liter of water it amounts to about 10⁶⁵ bits, many orders of magnitude greater than the actual physical entropy. Practical applications are still limited, but the bound sets fundamental limits for quantum computers and data storage density.

In practice

This limit determines how much data can be stored in quantum computers and explains why black holes are the most capacious information stores in the Universe.

Fun fact

The Bekenstein bound for the human brain is about 10⁷⁰ bits. That's billions of times more than all data ever stored by humanity. So our brain is still far from saturation, even though it operates on classical, not quantum, principles.

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Formulas

$$S_A = rac{ ext{Area}( ext{minimal surface})}{4G_N}$$
Entanglement entropy of a boundary region equals its minimal surface area in the bulk.
What is in the formula
S_AvariableEntanglement entropy of boundary region A — entanglement entropy
Area(minimal surfacevariableArea of the minimal surface in the bulk homologous to A — area square metre
G_NconstantNewton's gravitational constant in the bulk — gravitational constant = 6.67430e-11 cubic metre per kilogram per second squared
s_a=\frac{{area}(\gamma_a)}{4g} · 2605.02446v2
$$O(g(n))$$
Describes an upper bound on algorithm runtime.
What is in the formula
gvariablefunction whose growth is being bounded
nvariableinput size parameter
Ooperatorasymptotic upper bound up to constant factor — big o notation
o(k|w||q|+|δ|) · 2607.00742
o(|\sigma|n^{ω}) · 2607.00742
$$M(s) \ge (a s)^{b s}$$
A quantity grows faster than exponential, like s^{b s}.
What is in the formula
Mvariablea quantity that grows with parameter s
svariablea non-negative integer or real parameter indexing the growth
avariablea positive constant
bvariablea positive constant determining the growth exponent
m(s)\ge(as)^{3s/2} · 2607.00963
$$B = \prod_{k=1}^N \frac{P(D_k|H_1)}{P(D_k|H_0)}$$
Bayes factor accumulates evidence for hypotheses.
What is in the formula
BvariableBayes factor — bayes factor
D_kvariablek-th data point — data
H_1variablealternative hypothesis — hypothesis
H_0variablenull hypothesis — hypothesis
Nvariablenumber of data points — sample size
∏operatormultiplication of terms for k=1 to N — product operator
b_n=\prod_{k=1}^n\frac{p(d_k|h_1)}{p(d_k|h_0)} · 2505.00950v2
δ\lnz=\lnz_{model1}-\lnz_{model2} · 2607.01699v1
b^i_j=\frac{p(d|h_i)}{p(d|h_j)} · 2607.04663v1
k=\frac{p(x\mid\mathcal{m}_1)}{p(x\mid\mathcal{m}_0)} · 2607.20260v1
$$\Delta\theta \geq \frac{1}{\sqrt{\mathcal{F}(\rho_\theta)}}$$
Estimation error is bounded by inverse square root of Fisher information.
What is in the formula
\Delta\thetavariableStandard deviation of the estimator for parameter theta — standard deviation
\mathcal{F}(\rho_\thvariableQuantum Fisher information of the state rho_theta — quantum fisher information
δθ\geq\frac{1}{\sqrt{\mathcal{f}(ρ_θ)}} · 2505.01506v2
$$S \leq k \ln W$$
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

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