The fluctuation–dissipation theorem (FDT) or fluctuation–dissipation relation (FDR) is a powerful tool in statistical physics for predicting the behavior of systems that obey detailed balance. Given that a system obeys detailed balance, the theorem is a proof that thermodynamic fluctuations in a physical variable predict the response quantified by the admittance or impedance (to be intended in their general sense, not only in electromagnetic terms) of the same physical variable (like voltage, temperature difference, etc.), and vice versa. The fluctuation–dissipation theorem applies both to classical and quantum mechanical systems.
The fluctuation–dissipation theorem was proven by Herbert Callen and Theodore Welton in 1951^{[1]} and expanded by Ryogo Kubo. There are antecedents to the general theorem, including Einstein's explanation of Brownian motion^{[2]} during his annus mirabilis and Harry Nyquist's explanation in 1928 of Johnson noise in electrical resistors.^{[3]}
Qualitative overview and examples
The fluctuation–dissipation theorem says that when there is a process that dissipates energy, turning it into heat (e.g., friction), there is a reverse process related to thermal fluctuations. This is best understood by considering some examples:
 Drag and Brownian motion
 If an object is moving through a fluid, it experiences drag (air resistance or fluid resistance). Drag dissipates kinetic energy, turning it into heat. The corresponding fluctuation is Brownian motion. An object in a fluid does not sit still, but rather moves around with a small and rapidlychanging velocity, as molecules in the fluid bump into it. Brownian motion converts heat energy into kinetic energy—the reverse of drag.
 Resistance and Johnson noise
 If electric current is running through a wire loop with a resistor in it, the current will rapidly go to zero because of the resistance. Resistance dissipates electrical energy, turning it into heat (Joule heating). The corresponding fluctuation is Johnson noise. A wire loop with a resistor in it does not actually have zero current, it has a small and rapidlyfluctuating current caused by the thermal fluctuations of the electrons and atoms in the resistor. Johnson noise converts heat energy into electrical energy—the reverse of resistance.
 Light absorption and thermal radiation
 When light impinges on an object, some fraction of the light is absorbed, making the object hotter. In this way, light absorption turns light energy into heat. The corresponding fluctuation is thermal radiation (e.g., the glow of a "red hot" object). Thermal radiation turns heat energy into light energy—the reverse of light absorption. Indeed, Kirchhoff's law of thermal radiation confirms that the more effectively an object absorbs light, the more thermal radiation it emits.
Examples in detail
The fluctuation–dissipation theorem is a general result of statistical thermodynamics that quantifies the relation between the fluctuations in a system that obeys detailed balance and the response of the system to applied perturbations.
Brownian motion
For example, Albert Einstein noted in his 1905 paper on Brownian motion that the same random forces that cause the erratic motion of a particle in Brownian motion would also cause drag if the particle were pulled through the fluid. In other words, the fluctuation of the particle at rest has the same origin as the dissipative frictional force one must do work against, if one tries to perturb the system in a particular direction.
From this observation Einstein was able to use statistical mechanics to derive the Einstein–Smoluchowski relation
which connects the diffusion constant D and the particle mobility μ, the ratio of the particle's terminal drift velocity to an applied force. k_{B} is the Boltzmann constant, and T is the absolute temperature.
Thermal noise in a resistor
In 1928, John B. Johnson discovered and Harry Nyquist explained Johnson–Nyquist noise. With no applied current, the meansquare voltage depends on the resistance , , and the bandwidth over which the voltage is measured:^{[4]}
This observation can be understood through the lens of the fluctuationdissipation theorem. Take, for example, a simple circuit consisting of a resistor with a resistance and a capacitor with a small capacitance . Kirchhoff's voltage law yields
and so the response function for this circuit is
In the lowfrequency limit , its imaginary part is simply
which then can be linked to the power spectral density function of the voltage via the fluctuationdissipation theorem
The Johnson–Nyquist voltage noise was observed within a small frequency bandwidth centered around . Hence
General formulation
The fluctuation–dissipation theorem can be formulated in many ways; one particularly useful form is the following:^{[citation needed]}.
Let be an observable of a dynamical system with Hamiltonian subject to thermal fluctuations. The observable will fluctuate around its mean value with fluctuations characterized by a power spectrum . Suppose that we can switch on a timevarying, spatially constant field which alters the Hamiltonian to . The response of the observable to a timedependent field is characterized to first order by the susceptibility or linear response function of the system
where the perturbation is adiabatically (very slowly) switched on at .
The fluctuation–dissipation theorem relates the twosided power spectrum (i.e. both positive and negative frequencies) of to the imaginary part of the Fourier transform of the susceptibility :
Which holds under the Fourier transform convention . The lefthand side describes fluctuations in , the righthand side is closely related to the energy dissipated by the system when pumped by an oscillatory field .
This is the classical form of the theorem; quantum fluctuations are taken into account by replacing with (whose limit for is ). A proof can be found by means of the LSZ reduction, an identity from quantum field theory.^{[citation needed]}
The fluctuation–dissipation theorem can be generalized in a straightforward way to the case of spacedependent fields, to the case of several variables or to a quantummechanics setting.^{[1]}
Derivation
Classical version
We derive the fluctuation–dissipation theorem in the form given above, using the same notation. Consider the following test case: the field f has been on for infinite time and is switched off at t=0
where is the Heaviside function. We can express the expectation value of by the probability distribution W(x,0) and the transition probability
The probability distribution function W(x,0) is an equilibrium distribution and hence given by the Boltzmann distribution for the Hamiltonian
where . For a weak field , we can expand the righthand side
here is the equilibrium distribution in the absence of a field. Plugging this approximation in the formula for yields

(*)
where A(t) is the autocorrelation function of x in the absence of a field:
Note that in the absence of a field the system is invariant under timeshifts. We can rewrite using the susceptibility of the system and hence find with the above equation (*)
Consequently,

(**)
To make a statement about frequency dependence, it is necessary to take the Fourier transform of equation (**). By integrating by parts, it is possible to show that
Since is real and symmetric, it follows that
Finally, for stationary processes, the Wiener–Khinchin theorem states that the twosided spectral density is equal to the Fourier transform of the autocorrelation function:
Therefore, it follows that
Quantum version
The fluctuationdissipation theorem relates the correlation function of the observable of interest (a measure of fluctuation) to the imaginary part of the response function in the frequency domain (a measure of dissipation). A link between these quantities can be found through the socalled Kubo formula^{[5]}
which follows, under the assumptions of the linear response theory, from the time evolution of the ensemble average of the observable in the presence of a perturbing source. Once Fourier transformed, the Kubo formula allows writing the imaginary part of the response function as
In the canonical ensemble, the second term can be reexpressed as
where in the second equality we repositioned using the cyclic property of trace. Next, in the third equality, we inserted next to the trace and interpreted as a time evolution operator with imaginary time interval . The imaginary time shift turns into a factor after Fourier transform
and thus the expression for can be easily rewritten as the quantum fluctuationdissipation relation ^{[6]}
where the power spectral density is the Fourier transform of the autocorrelation and is the BoseEinstein distribution function. The same calculation also yields
thus, differently from what obtained in the classical case, the power spectral density is not exactly frequencysymmetric in the quantum limit. Consistently, has an imaginary part originating from the commutation rules of operators.^{[7]} The additional "" term in the expression of at positive frequencies can also be thought of as linked to spontaneous emission. An often cited result is also the symmetrized power spectral density
The "" can be thought of as linked to quantum fluctuations, or to zeropoint motion of the observable . At high enough temperatures, , i.e. the quantum contribution is negligible, and we recover the classical version.
Violations in glassy systems
While the fluctuation–dissipation theorem provides a general relation between the response of systems obeying detailed balance, when detailed balance is violated comparison of fluctuations to dissipation is more complex. Below the so called glass temperature , glassy systems are not equilibrated, and slowly approach their equilibrium state. This slow approach to equilibrium is synonymous with the violation of detailed balance. Thus these systems require large timescales to be studied while they slowly move toward equilibrium.
To study the violation of the fluctuationdissipation relation in glassy systems, particularly spin glasses, performed numerical simulations of macroscopic systems (i.e. large compared to their correlation lengths) described by the threedimensional EdwardsAnderson model using supercomputers.^{[8]} In their simulations, the system is initially prepared at a high temperature, rapidly cooled to a temperature below the glass temperature , and left to equilibrate for a very long time under a magnetic field . Then, at a later time , two dynamical observables are probed, namely the response function
Their results confirm the expectation that as the system is left to equilibrate for longer times, the fluctuationdissipation relation is closer to be satisfied.
In the mid1990s, in the study of dynamics of spin glass models, a generalization of the fluctuation–dissipation theorem was discovered that holds for asymptotic nonstationary states, where the temperature appearing in the equilibrium relation is substituted by an effective temperature with a nontrivial dependence on the time scales.^{[9]} This relation is proposed to hold in glassy systems beyond the models for which it was initially found.
See also
 Nonequilibrium thermodynamics
 Green–Kubo relations
 Onsager reciprocal relations
 Equipartition theorem
 Boltzmann distribution
 Dissipative system
Notes
 ^ ^{a} ^{b} H.B. Callen; T.A. Welton (1951). "Irreversibility and Generalized Noise". Physical Review. 83 (1): 34–40. Bibcode:1951PhRv...83...34C. doi:10.1103/PhysRev.83.34.
 ^ Einstein, Albert (May 1905). "Über die von der molekularkinetischen Theorie der Wärme geforderte Bewegung von in ruhenden Flüssigkeiten suspendierten Teilchen". Annalen der Physik. 322 (8): 549–560. Bibcode:1905AnP...322..549E. doi:10.1002/andp.19053220806.
 ^ Nyquist H (1928). "Thermal Agitation of Electric Charge in Conductors". Physical Review. 32 (1): 110–113. Bibcode:1928PhRv...32..110N. doi:10.1103/PhysRev.32.110.
 ^ Blundell, Stephen J.; Blundell, Katherine M. (2009). Concepts in thermal physics. OUP Oxford.
 ^ Kubo R (1966). "The fluctuationdissipation theorem". Reports on Progress in Physics. 29 (1): 255–284. Bibcode:1966RPPh...29..255K. doi:10.1088/00344885/29/1/306. S2CID 250892844.
 ^ Hänggi Peter, Ingold GertLudwig (2005). "Fundamental aspects of quantum Brownian motion". Chaos: An Interdisciplinary Journal of Nonlinear Science. 15 (2): 026105. arXiv:quantph/0412052. Bibcode:2005Chaos..15b6105H. doi:10.1063/1.1853631. PMID 16035907. S2CID 9787833.
 ^ Clerk, A. A.; Devoret, M. H.; Girvin, S. M.; Marquardt, Florian; Schoelkopf, R. J. (2010). "Introduction to Quantum Noise, Measurement and Amplification". Reviews of Modern Physics. 82 (2): 1155. arXiv:0810.4729. Bibcode:2010RvMP...82.1155C. doi:10.1103/RevModPhys.82.1155. S2CID 119200464.
 ^ BaityJesi Marco, Calore Enrico, Cruz Andres, Antonio Fernandez Luis, Miguel GilNarvión José, GordilloGuerrero Antonio, Iñiguez David, Maiorano Andrea, Marinari Enzo, MartinMayor Victor, MonforteGarcia Jorge, Muñoz Sudupe Antonio, Navarro Denis, Parisi Giorgio, PerezGaviro Sergio, RicciTersenghi Federico, Jesus RuizLorenzo Juan, Fabio Schifano Sebastiano, Seoane Beatriz, Tarancón Alfonso, Tripiccione Raffaele, Yllanes David (2017). "A staticsdynamics equivalence through the fluctuation–dissipation ratio provides a window into the spinglass phase from nonequilibrium measurements". Proceedings of the National Academy of Sciences. 114 (8): 1838–1843. arXiv:1610.01418. Bibcode:2017PNAS..114.1838B. doi:10.1073/pnas.1621242114. PMC 5338409. PMID 28174274.
{{cite journal}}
: CS1 maint: multiple names: authors list (link)  ^ Cugliandolo L. F.; Kurchan J. (1993). "Analytical solution of the offequilibrium dynamics of a longrange spinglass model". Physical Review Letters. 71 (1): 173–176. arXiv:condmat/9303036. Bibcode:1993PhRvL..71..173C. doi:10.1103/PhysRevLett.71.173. PMID 10054401. S2CID 8591240.
References
 H. B. Callen, T. A. Welton (1951). "Irreversibility and Generalized Noise". Physical Review. 83 (1): 34–40. Bibcode:1951PhRv...83...34C. doi:10.1103/PhysRev.83.34.
 L. D. Landau, E. M. Lifshitz (1980). Statistical Physics. Course of Theoretical Physics. Vol. 5 (3 ed.).
 Umberto Marini Bettolo Marconi; Andrea Puglisi; Lamberto Rondoni; Angelo Vulpiani (2008). "FluctuationDissipation: Response Theory in Statistical Physics". Physics Reports. 461 (4–6): 111–195. arXiv:0803.0719. Bibcode:2008PhR...461..111M. doi:10.1016/j.physrep.2008.02.002. S2CID 118575899.
Further reading
 Audio recording of a lecture by Prof. E. W. Carlson of Purdue University
 Kubo's famous text: Fluctuationdissipation theorem
 Weber J (1956). "Fluctuation Dissipation Theorem". Physical Review. 101 (6): 1620–1626. arXiv:0710.4394. Bibcode:1956PhRv..101.1620W. doi:10.1103/PhysRev.101.1620.
 Felderhof BU (1978). "On the derivation of the fluctuationdissipation theorem". Journal of Physics A. 11 (5): 921–927. Bibcode:1978JPhA...11..921F. doi:10.1088/03054470/11/5/021.
 Cristani A, Ritort F (2003). "Violation of the fluctuationdissipation theorem in glassy systems: basic notions and the numerical evidence". Journal of Physics A. 36 (21): R181–R290. arXiv:condmat/0212490. Bibcode:2003JPhA...36R.181C. doi:10.1088/03054470/36/21/201. S2CID 14144683.
 Chandler D (1987). Introduction to Modern Statistical Mechanics. Oxford University Press. pp. 231–265. ISBN 9780195042771.
 Reichl LE (1980). A Modern Course in Statistical Physics. Austin TX: University of Texas Press. pp. 545–595. ISBN 0292750803.
 Plischke M, Bergersen B (1989). Equilibrium Statistical Physics. Englewood Cliffs, NJ: Prentice Hall. pp. 251–296. ISBN 0132832763.
 Pathria RK (1972). Statistical Mechanics. Oxford: Pergamon Press. pp. 443, 474–477. ISBN 0080189946.
 Huang K (1987). Statistical Mechanics. New York: John Wiley and Sons. pp. 153, 394–396. ISBN 0471815187.
 Callen HB (1985). Thermodynamics and an Introduction to Thermostatistics. New York: John Wiley and Sons. pp. 307–325. ISBN 0471862568.
 Mazonka, Oleg (2016). "Easy as Pi: The FluctuationDissipation Relation" (PDF). Journal of Reference. 16.