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<strong>Goal:</strong> This lecture is dedicated to a classical model in disordered systems: the directed polymer in random media. It has been introduced to model vortices in superconductors or domain walls in magnetic films. We will focus on algorithms that identify the ground state or compute the free energy at temperature <math>T</math>, as well as on the Cole–Hopf transformation that maps this model to the KPZ equation.


<strong>Goal: </strong> This lecture is dedicated to a classical model in disordered systems: the directed polymer in random media. It has been introduced to model vortices in superconductur or domain wall in magnetic film. We will focus here on the algorithms that identify the ground state or compute the free energy at temperature T, as well as, on the Cole-Hopf transformation that map this model on the KPZ equation. 
= Directed Polymers (''d = 1'') =


The configuration is described by a vector function <math>\vec{x}(t)</math>, where <math>t</math> is the internal coordinate. The polymer lives in <math>D = 1 + N</math> dimensions.


=Polymers, interfaces and manifolds in random media=
Examples: vortex lines, DNA strands, fronts.
We consider the following potential energy
<center> <math>
E_{pot}= \int dr \frac{1}{2}(\nabla h)^2 + V(r,h)
</math></center>
The first term represents the elasticity of the manifold and the second term is the quenched disorder, due to the impurities. In general, the medium is D-dimensional, the internal coordinate of the manifold is d-dimensional and the height filed is N-dimensional. Hence,the following equations always holds:
<center> <math>
D=d+N
</math></center>
In practice, we will study two cases:
* Directed Polymers (<math>d=1</math>), <math> D=1+N </math>. Examples are vortices, fronts...
* Elastic interfaces (<math>N=1</math>), <math> D=d+1 </math>.  Examples are domain walls...


Today we restrict to polymers. Note that they are directed because their configuration <math>
Although polymers may form loops, we restrict to directed polymers, i.e., configurations without overhangs or backward turns.
h(r) </math> is uni-valuated.  
It is useful to study the model using the following change of variable
<center> <math>
h \to x, \quad r\to t
</math></center>


=Directed polymers=
= Directed Polymers on a lattice =


==Dijkstra Algorithm and transfer matrix==
[[File:SketchDPRM.png|thumb|left|Sketch of the discrete Directed Polymer model. At each time the polymer grows either one step left or one step right. A random energy <math>V(\tau,x)</math> is associated to each node and the total energy is simply <math>E[x(\tau)] = \sum_{\tau=0}^t V(\tau,x)</math>.]]


We introduce a lattice model for the directed polymer (see figure). In a companion notebook we provide the implementation of the powerful Dijkstra algorithm. Dijkstra allows one to identify the minimal energy among the exponential number of configurations <math>x(\tau)</math>:
<math display="block">
E_{\min} = \min_{x(\tau)} E[x(\tau)].
</math>


We are also interested in the ground state configuration <math>x_{\min}(\tau)</math>. For both quantities we expect scale invariance with two exponents <math>\theta</math>, <math>\zeta</math> for the energy and for the roughness:
<math display="block">
E_{\min} = c_\infty t + \kappa_1 t^{\theta}\chi,
\quad
x_{\min}(t/2) \sim \kappa_2 t^{\zeta}\tilde\chi.
</math>


[[File:SketchDPRM.png|thumb|left|Sketch of the discrete Directed Polymer model. At each time the polymer grows either one step left either one step right.  A random energy <math> V(\tau,x)</math> is associated at each node and the total energy is simply <math> E[x(\tau)] =\sum_{\tau=0}^t V(\tau,x)</math>. ]]
<strong>Universal exponents:</strong> Both <math>\theta</math> and <math>\zeta</math> are independent of the lattice, the disorder distribution, the elastic constants, or the boundary conditions.


<strong>Non-universal constants:</strong> <math>c_\infty</math>, <math>\kappa_1</math>, <math>\kappa_2</math> are of order 1 and depend on the lattice, the disorder distribution, the elastic constants, etc. However <math>c_\infty</math> is independent of the boundary conditions.


We introduce a lattice model for the directed polymer (see figure). In a companion notebook we provide the implementation of the powerful Dijkstra algorithm.
<strong>Universal distributions:</strong> <math>\chi</math>, <math>\tilde\chi</math> are universal, but depend on the boundary conditions. Starting from 2000, a remarkable connection has been revealed between this model and the smallest eigenvalues of random matrices. In particular, we discuss two different boundary conditions:


Dijkstra allows to identify the minimal  energy among the exponential number of  configurations <math> x(\tau)</math>
* <strong>Droplet</strong>: <math>x(\tau=0) = x(\tau=t) = 0</math>. In this case, up to rescaling, <math>\chi</math> is distributed as the smallest eigenvalue of a GUE random matrix (Tracy–Widom distribution <math>F_2(\chi)</math>).
<center> <math>
E_{\min} = \min_{x(\tau)} E[x(\tau)].  
</math></center>


We are also interested in the ground state configuration  <math> x_{\min}(\tau) </math>.
* <strong>Flat</strong>: <math>x(\tau=0) = 0</math> while the other end <math>x(\tau=t)</math> is free. In this case, up to rescaling, <math>\chi</math> is distributed as the smallest eigenvalue of a GOE random matrix (Tracy–Widom distribution <math>F_1(\chi)</math>).
For both quantities we expect scale invariance with two exponents  <math> \theta, \zeta</math> for the energy and for the roughness
<center>
<math>
E_{\min} = c_\infty t + b_\infty t^{\theta}\chi,  \quad x_{\min}(t/2)) \sim  a_\infty t^{\zeta} \tilde \chi
</math></center>


<strong>Universal exponents: </strong> Both  <math> \theta, \zeta </math> are  Independent of the lattice, the disorder distribution, the elastic constants, or the boudanry conditions.  Note that  <math> \omega= \theta </math>, while for an interface <math> \omega= d \theta </math>.
=== Entropy and scaling relation ===


<strong>Non-universal constants: </strong> <math> c_\infty,b_\infty, a_\infty </math> are of  order 1 and depend on the lattice, the disorder distribution, the elastic constants... However  <math> c_\infty  </math> is independent on the boudanry conditions!
It is useful to compute the entropy
<math display="block">
\text{Entropy}= \ln\binom{t}{\frac{t-x}{2}} \approx t \ln 2 -\frac{x^2}{2 t} + O(x^4).
</math>
From which one could guess from dimensional analysis
<math display="block">
\theta = 2\zeta - 1.
</math>
This relation is actually exact also for the continuum model.
 
= Directed polymers in the continuum =
 
We now reanalyze the previous problem in the presence of quenched disorder. Instead of discussing the case of interfaces, we will focus on directed polymers.


<strong>Universal distributions: </strong> <math> \chi, \tilde \chi </math> are instead universal, but depends on the boundary condtions. Starting from 2000 a magic connection has been revealed between this model and the smallest eigenvalues of random matrices. In particular I discuss two different boundary conditions:
Let us consider polymers <math>x(\tau)</math> of length <math>t</math>. The energy associated with a given polymer configuration can be written as
<math display="block">
E[x(\tau)] = \int_0^t d\tau \left[ \frac{1}{2}\left(\frac{dx}{d\tau}\right)^2 + V(x(\tau),\tau) \right].
</math>
The first term describes the elastic energy of the polymer, while the second one is the disordered potential, which we assume to be
<math display="block">
\overline{V(x,\tau)} = 0,
\qquad
\overline{V(x,\tau)V(x',\tau')} = D\,\delta(x-x')\,\delta(\tau-\tau').
</math>
where <math>D</math> is the disorder strength.


* <strong>Droplet</strong>: <math> x(\tau=0) = x(\tau=t) = 0 </math>. In this case, up to rescaling,  <math> \chi</math> is distributed as the smallest eigenvalue of a GUE random matrix (Tracy Widom distribution <math>F_2(\chi) </math>)
== Polymer partition function and propagator of a quantum particle ==


* <strong> Flat</strong>: <math> x(\tau=0) = 0 </math> while the other end <math> x(\tau=t) </math> is free. In this case, up to rescaling,  <math> \chi</math> is distributed as the smallest eigenvalue of a GOE random matrix (Tracy Widom distribution <math>F_1(\chi) </math>)
Let us consider polymers starting at <math>0</math>, ending at <math>x</math> and at thermal equilibrium at temperature <math>T</math>. The partition function of the model reads
===Entropy and scaling relation===
<math display="block">
Z(x,t) = \int_{x(0)=0}^{x(t)=x} \mathcal{D}x(\tau)\,
\exp\!\left[-\frac{1}{T}\int_0^t d\tau\left(\frac{1}{2}(\partial_\tau x)^2 + V(x(\tau),\tau)\right)\right].
</math>
Here, the partition function is written as a sum over all possible paths, corresponding to all polymer configurations that start at <math>0</math> and end at <math>x</math>, weighted by the appropriate Boltzmann factor.


It is useful to compute the entropy
Let's perform the following change of variables: <math>\tau = i t'</math>. We also identify <math>T</math> with <math>\hbar</math> and <math>\tilde t = - i t</math> as the time.
<center>
<math display="block">
<math>
Z(x,\tilde t) = \int_{x(0)=0}^{x(\tilde t)=x} \mathcal{D}x(t')\,
\text{Entropy}= \ln\binom{t}{\frac{t-x}{2}} \approx t \ln 1 -\frac{x^2}{t} +O(x^4)
\exp\!\left[\frac{i}{\hbar}\int_0^{\tilde t} dt'\left(\frac{1}{2}(\partial_{t'} x)^2 - V(x(t'),t')\right)\right].
</math></center>
</math>
From which we infer
 
<center>
Note that <math>S[x] = \int_0^{\tilde t} dt'\left(\frac{1}{2}(\partial_{t'} x)^2 - V(x(t'),t')\right)</math> is the classical action of a particle with kinetic energy <math>\frac{1}{2}(\partial_{t'}x)^2</math> and time-dependent potential <math>V(x(t'),t')</math>, evolving from time zero to time <math>\tilde t</math>. From the Feynman path integral formulation, <math>Z(x,\tilde t)</math> is the propagator of the quantum particle.
<math>
\theta=2 \zeta-1
</math></center>


==Back to the continuum model==
=== Feynman–Kac formula ===


Let us consider polymers <math>
Let's derive the Feynman–Kac formula for <math>Z(x,t)</math> in the general case:
x(\tau) </math>  of length  <math>
t </math>, starting in  <math>0 </math>  and ending in <math>x </math> and at thermal equlibrium at  temperature <math>T</math>. The partition function of the model writes as
<center> <math>
Z[x,t] =\int_{x(0)=0}^{x(t)=x} {\cal D} x(\tau) \exp\left[- \frac{1}{T} \int_0^t d \tau \frac{1}2(\partial_\tau x)^2 +V(x(\tau),\tau)\right]
</math></center>
We remark that the energy of the polymer is equivalent to the action of a particle: the term  <math>\frac{1}2(\partial_\tau x)^2</math>  is the kinetic energy  and <math> - V(x(\tau),\tau)</math> is a time dependent potential. For simplicity, we assume a white noise, <math> \overline{V(x,\tau) V(x',\tau')} = D \delta(x-x') \delta(\tau-\tau') </math>.


Within this analogy, <math> Z[x,t]</math>, is the propagator of a quantum particle, but in the imaginary time (as <math>i/\hbar</math> is replaceed by <math>-1/T</math>). Hence, in absence of disorder we recover the diffusion propagator of the free particle.
<center> <math>
Z_{\text{free}}[x,t]=\frac{e^{-x^2/(2Tt)}}{\sqrt{2Tt}}
</math></center>
== Feynman-Kac foruma==
Let's derive the Feyman Kac formula for  <math>Z[x_t,t]</math> in the general case:
* First, focus on free paths and introduce the following probability
* First, focus on free paths and introduce the following probability
<center> <math>
<math display="block">
P[A,x,t] =\int_{x(0)=0}^{x(t)=x} {\cal D} x(\tau) \exp\left[- \frac{1}{T} \int_0^t d \tau \frac{1}2(\partial_\tau x)^2 \right] \delta\left(   \int_0^t d \tau V(x(\tau),\tau)-A \right)
P[A,x,t] = \int_{x(0)=0}^{x(t)=x} \mathcal{D}x(\tau)\,
</math></center>
\exp\!\left[-\frac{1}{T}\int_0^t d\tau\,\frac{1}{2}(\partial_\tau x)^2\right]\,
* Second, the moments generating function  
\delta\!\left(\int_0^t d\tau\,V(x(\tau),\tau) - A\right).
<center> <math>
</math>
Z_p[x ,t] = \int_{-\infty}^\infty d A e^{-p A} P[A,x,t] =\int_{x(0)=0}^{x(t)=x} {\cal D} x(\tau) e^{-\frac{1}{T} \int_0^t d \tau \frac{1}2(\partial_\tau x)^2 -p \int_0^t d \tau V(x(\tau),\tau)}
 
</math></center>
* Second, the moment generating function
* Third, the backward approach. Consider free paths evolving up to <math>t+dt</math> and reaching <math>x</math> :
<math display="block">
<center> <math>
Z_p(x,t) = \int_{-\infty}^{\infty} dA\,e^{-pA}P[A,x,t]
Z_p(x,t+dt)= \left\langle e^{-p \int_0^{t+dt} d \tau V(x(\tau),\tau)}\right\rangle= \left\langle e^{-p \int_0^{t} d \tau V(x(\tau),\tau)}\right\rangle e^{-p V(x,t) d t } =[1 -p V(x,t) d t +\dots]\left\langle Z_p(x-\Delta x,t) \right\rangle_{\Delta x}
= \int_{x(0)=0}^{x(t)=x} \mathcal{D}x(\tau)\,
</math></center>
\exp\!\left[-\frac{1}{T}\int_0^t d\tau\,\frac{1}{2}(\partial_\tau x)^2
Here <math> \langle \ldots \rangle</math> is the average over all free paths, while <math> \langle \ldots \rangle_{\Delta x}</math> is the average over the last jump, namely   <math> \langle \Delta x \rangle=0
- p\int_0^t d\tau\,V(x(\tau),\tau)\right].
</math> and <math> \langle \Delta x^2 \rangle=T d t  </math>.
</math>
 
* Third, consider free paths evolving up to <math>t+dt</math> and reaching <math>x</math>:
<math display="block">
Z_p(x,t+dt)
= \left\langle e^{-p\int_0^{t+dt} d\tau\,V(x(\tau),\tau)} \right\rangle
= \left\langle e^{-p\int_0^{t} d\tau\,V(x(\tau),\tau)} \right\rangle e^{-pV(x,t)dt}
= [1-pV(x,t)dt+\dots]\left\langle Z_p(x-\Delta x,t)\right\rangle_{\Delta x}.
</math>
Here <math>\langle\cdots\rangle</math> is the average over all free paths, while <math>\langle\cdots\rangle_{\Delta x}</math> is the average over the last jump, namely <math>\langle\Delta x\rangle=0</math> and <math>\langle\Delta x^2\rangle = T\,dt</math>.
 
* At the lowest order we have
* At the lowest order we have
<center> <math>
<math display="block">
Z_p(x,t+dt)= Z_p(x,t) +dt \left[ \frac{T}{2} \partial_x^2 Z_p -p V(x,t) Z_p \right] +O(dt^2)
Z_p(x,t+dt)
</math></center>
= Z_p(x,t) + dt\left[\frac{T}{2}\partial_x^2 Z_p - pV(x,t)Z_p\right] + O(dt^2).
Replacing <math> p=1/T</math> we obtain the partition function is the solution of the Schrodinger-like equation:
</math>
<center> <math>
 
\partial_t Z =- \hat H Z =\frac{T}{2}\frac{d^2 Z}{d x^2} - \frac{V(x,\tau)}{T} Z  
Replacing <math>p=1/T</math> we obtain that the partition function is the solution of the Schrödinger-like equation:
</math></center>
<math display="block">
The initial condition is  <math>  Z[x,t=0]=\delta(x) </math>.
\partial_t Z(x,t)
This equation is a diffusive equation with multiplicative noise. The EW of the previous lecture is a diffusive equation with additive noise. The Cole Hopf transformation allows to map the diffusive equation with multiplicative noise in a non-linear equation with additive noise: the KPZ euqation. Hence, all KPZ results can be used for the directed polymer.
= -\hat H Z
= -\left[-\frac{T}{2}\frac{d^2}{dx^2} + \frac{V(x,t)}{T}\right] Z(x,t),
\qquad
Z(x,t=0)=\delta(x).
</math>
 
=== Remarks ===
 
<Strong>Remark 1:</Strong>
 
This equation is a diffusive equation with multiplicative noise <math>V(x,t)/T</math>. Edwards–Wilkinson is instead a diffusive equation with additive noise.
 
<Strong>Remark 2:</Strong>
 
This Hamiltonian is time dependent because of the multiplicative noise <math>V(x,t)/T</math>. For a <Strong>time independent</Strong> Hamiltonian, we can use the spectrum of the operator. In general we will have two parts:
 
* A discrete set of eigenvalues <math>E_n</math> with eigenstates <math>\psi_n(x)</math>
* A continuum part where the states <math>\psi_E(x)</math> have energy <math>E</math>. We define the density of states <math>\rho(E)</math>, such that the number of states with energy in <math>(E,E+dE)</math> is <math>\rho(E)\,dE</math>.
 
In this case <math>Z(x,t)</math> can be written as the sum of two contributions:
<math display="block">
Z(x,t)
= \left(e^{-\hat H t}\right)_{0\to x}
= \sum_n \psi_n(0)\psi_n^*(x)e^{-E_n t}
+ \int_0^\infty dE\,\rho(E)\,\psi_E(0)\psi_E^*(x)e^{-Et}.
</math>
 
In absence of disorder, one can find the propagator of the free particle, that, in the original variables, writes:
<math display="block">
Z_{\text{free}}(x,t)=\frac{e^{-x^2/(2Tt)}}{\sqrt{2\pi Tt}}.
</math>
 
==== Hints: free particle in 1D ====
 
For a free particle in one dimension the Hamiltonian is <math>\hat H = -\frac{T}{2}\,\partial_x^2</math>.
 
'''Spectrum.'''
The spectrum is purely continuous. The eigenstates are plane waves
<math display="block">
\psi_k(x)=\frac{1}{\sqrt{2\pi}}e^{ikx},
\qquad
E_k=\frac{T k^2}{2},
</math>
with <math>k\in\mathbb{R}</math>. The states are delocalized and satisfy Dirac delta normalization
<math display="block">
\int_{-\infty}^{\infty} dx\,\psi_{k'}^*(x)\psi_k(x)=\delta(k-k').
</math>
 
'''Energy representation and density of states.'''
For a given energy <math>E>0</math> there are two degenerate states,
<math display="block">
\psi_E^{\pm}(x)=\frac{1}{\sqrt{2\pi}}\,e^{\pm i\sqrt{2E/T}\,x}.
</math>
The density of states is obtained from
<math display="block">
\rho(E)=\int_{-\infty}^{\infty} dk\,\delta(E-E_k),
\qquad
E_k=\frac{T k^2}{2}.
</math>
 
'''Propagator.'''
Using the spectral decomposition one can write
<math display="block">
Z(x,t)
=\int_0^{\infty} dE\,\rho(E)
\sum_{\sigma=\pm}
\psi_E^{\sigma}(0)\psi_E^{\sigma *}(x)\,e^{-Et}.
</math>
Evaluating the resulting Gaussian integral yields
<math display="block">
Z_{\text{free}}(x,t)=\frac{e^{-x^2/(2Tt)}}{\sqrt{2\pi Tt}}.
</math>
 
Useful identity:
<math display="block">
\int_{-\infty}^{\infty} dx\,e^{-(a x^2+b x)}
=\sqrt{\frac{\pi}{a}}\,e^{\,b^2/(4a)},\qquad a>0.
</math>
 
== Cole Hopf Transformation ==
 
Replacing
* <math>T = 2\nu</math>
* <math>x = r</math>
* <math>Z(x,t) = \exp\!\left(\frac{\lambda}{2\nu}h(r,t)\right)</math>
* <math>-V(x,t)=\lambda\,\eta(r,t)</math>
 
you get
<math display="block">
\partial_t h(r,t)= \nu \nabla^2 h(r,t)+ \frac{\lambda}{2}(\nabla h)^2 + \eta(r,t).
</math>
The KPZ equation!
 
We can establish a KPZ/Directed polymer dictionary, valid in any dimension. Let us remark that the free energy of the polymer is
<math display="block">
F = -T\ln Z(x,t) = -\frac{1}{\lambda}h(r,t).
</math>
At low temperature, the free energy approaches the ground state energy <math>E_{\min}</math>.


== Cole Hopf Transformation==
Replacing
* <math>T =2 \nu </math>
* <math>  Z[x_t,t ;0 , 0] = \exp\left(\frac{\lambda}{2 \nu} h(x,t) \right) </math>
*  <math>- V(x,t)=\lambda  \eta(x,t) </math>
You get
<center> <math>
\partial_t h(r,t)= \nu \nabla^2 h(r,t)+ \frac{\lambda}{2} (\nabla h)^2 + \eta(r,t)
</math></center>
The KPZ equation! We can establish a KPZ/Directed polymer Dictionary
{| class="wikitable"
{| class="wikitable"
|+ Dictionary
|+ KPZ / Directed Polymer dictionary
|-
|-
! KPZ !! KPZ exponents !! Directed polymer !! Directed polymer exponents
! KPZ quantity !! KPZ scaling !! Directed polymer quantity !! Directed polymer scaling
|-
|-
| <math> x </math>|| <math> x \sim t^{1/z}</math>|| <math>x </math>|| <math>x\sim t^{\zeta}</math>
| <math>r</math>
| <math>r \sim t^{1/z}</math>
| <math>x</math>
| <math>x \sim t^{\zeta}</math>
|-
|-
| <math>t</math> ||<math>h(x,t) \sim t^{\alpha/z}</math> || <math>t</math>||
| <math>t</math>
| <math>h(r,t) \sim t^{\alpha/z}</math>
| <math>t</math>
| <math>\overline{(E_{\min}-\overline{E_{\min}})^2} \sim t^{2\theta}</math>
|-
|-
| <math>h</math> || <math>h(x,t) \sim x^{\alpha}</math>|| <math>E_{\min}</math> || <math> E_{\min} -c_\infty t \sim t^{\theta} </math>
| <math>h</math>
| <math>h(r,t) \sim r^{\alpha}</math>
| <math>F,\,E_{\min}</math>
| <math>\overline{(E_{\min}-\overline{E_{\min}})^2} \sim t^{2\theta}</math>
|}
|}
This dictionary is valid in any dimension. We conclude that  
 
<center> <math>
We conclude that
\theta =\alpha/z, \quad \zeta=1/z  
<math display="block">
</math></center>
\theta = \alpha/z,
Moreover, the scaling relation <math>
\quad
\theta =2 \zeta- 1  
\zeta = 1/z.
</math> is a reincarnation of the Galilean invariance <math>
</math>
\alpha +z =2
Moreover, the scaling relation <math>\theta = 2\zeta - 1</math> is a reincarnation of the Galilean invariance <math>\alpha + z = 2</math>.
</math>.

Latest revision as of 22:02, 1 March 2026

Goal: This lecture is dedicated to a classical model in disordered systems: the directed polymer in random media. It has been introduced to model vortices in superconductors or domain walls in magnetic films. We will focus on algorithms that identify the ground state or compute the free energy at temperature T, as well as on the Cole–Hopf transformation that maps this model to the KPZ equation.

Directed Polymers (d = 1)

The configuration is described by a vector function x(t), where t is the internal coordinate. The polymer lives in D=1+N dimensions.

Examples: vortex lines, DNA strands, fronts.

Although polymers may form loops, we restrict to directed polymers, i.e., configurations without overhangs or backward turns.

Directed Polymers on a lattice

Sketch of the discrete Directed Polymer model. At each time the polymer grows either one step left or one step right. A random energy V(τ,x) is associated to each node and the total energy is simply E[x(τ)]=τ=0tV(τ,x).

We introduce a lattice model for the directed polymer (see figure). In a companion notebook we provide the implementation of the powerful Dijkstra algorithm. Dijkstra allows one to identify the minimal energy among the exponential number of configurations x(τ): Emin=minx(τ)E[x(τ)].

We are also interested in the ground state configuration xmin(τ). For both quantities we expect scale invariance with two exponents θ, ζ for the energy and for the roughness: Emin=ct+κ1tθχ,xmin(t/2)κ2tζχ~.

Universal exponents: Both θ and ζ are independent of the lattice, the disorder distribution, the elastic constants, or the boundary conditions.

Non-universal constants: c, κ1, κ2 are of order 1 and depend on the lattice, the disorder distribution, the elastic constants, etc. However c is independent of the boundary conditions.

Universal distributions: χ, χ~ are universal, but depend on the boundary conditions. Starting from 2000, a remarkable connection has been revealed between this model and the smallest eigenvalues of random matrices. In particular, we discuss two different boundary conditions:

  • Droplet: x(τ=0)=x(τ=t)=0. In this case, up to rescaling, χ is distributed as the smallest eigenvalue of a GUE random matrix (Tracy–Widom distribution F2(χ)).
  • Flat: x(τ=0)=0 while the other end x(τ=t) is free. In this case, up to rescaling, χ is distributed as the smallest eigenvalue of a GOE random matrix (Tracy–Widom distribution F1(χ)).

Entropy and scaling relation

It is useful to compute the entropy Entropy=ln(ttx2)tln2x22t+O(x4). From which one could guess from dimensional analysis θ=2ζ1. This relation is actually exact also for the continuum model.

Directed polymers in the continuum

We now reanalyze the previous problem in the presence of quenched disorder. Instead of discussing the case of interfaces, we will focus on directed polymers.

Let us consider polymers x(τ) of length t. The energy associated with a given polymer configuration can be written as E[x(τ)]=0tdτ[12(dxdτ)2+V(x(τ),τ)]. The first term describes the elastic energy of the polymer, while the second one is the disordered potential, which we assume to be V(x,τ)=0,V(x,τ)V(x,τ)=Dδ(xx)δ(ττ). where D is the disorder strength.

Polymer partition function and propagator of a quantum particle

Let us consider polymers starting at 0, ending at x and at thermal equilibrium at temperature T. The partition function of the model reads Z(x,t)=x(0)=0x(t)=x𝒟x(τ)exp[1T0tdτ(12(τx)2+V(x(τ),τ))]. Here, the partition function is written as a sum over all possible paths, corresponding to all polymer configurations that start at 0 and end at x, weighted by the appropriate Boltzmann factor.

Let's perform the following change of variables: τ=it. We also identify T with and t~=it as the time. Z(x,t~)=x(0)=0x(t~)=x𝒟x(t)exp[i0t~dt(12(tx)2V(x(t),t))].

Note that S[x]=0t~dt(12(tx)2V(x(t),t)) is the classical action of a particle with kinetic energy 12(tx)2 and time-dependent potential V(x(t),t), evolving from time zero to time t~. From the Feynman path integral formulation, Z(x,t~) is the propagator of the quantum particle.

Feynman–Kac formula

Let's derive the Feynman–Kac formula for Z(x,t) in the general case:

  • First, focus on free paths and introduce the following probability

P[A,x,t]=x(0)=0x(t)=x𝒟x(τ)exp[1T0tdτ12(τx)2]δ(0tdτV(x(τ),τ)A).

  • Second, the moment generating function

Zp(x,t)=dAepAP[A,x,t]=x(0)=0x(t)=x𝒟x(τ)exp[1T0tdτ12(τx)2p0tdτV(x(τ),τ)].

  • Third, consider free paths evolving up to t+dt and reaching x:

Zp(x,t+dt)=ep0t+dtdτV(x(τ),τ)=ep0tdτV(x(τ),τ)epV(x,t)dt=[1pV(x,t)dt+]Zp(xΔx,t)Δx. Here is the average over all free paths, while Δx is the average over the last jump, namely Δx=0 and Δx2=Tdt.

  • At the lowest order we have

Zp(x,t+dt)=Zp(x,t)+dt[T2x2ZppV(x,t)Zp]+O(dt2).

Replacing p=1/T we obtain that the partition function is the solution of the Schrödinger-like equation: tZ(x,t)=H^Z=[T2d2dx2+V(x,t)T]Z(x,t),Z(x,t=0)=δ(x).

Remarks

Remark 1:

This equation is a diffusive equation with multiplicative noise V(x,t)/T. Edwards–Wilkinson is instead a diffusive equation with additive noise.

Remark 2:

This Hamiltonian is time dependent because of the multiplicative noise V(x,t)/T. For a time independent Hamiltonian, we can use the spectrum of the operator. In general we will have two parts:

  • A discrete set of eigenvalues En with eigenstates ψn(x)
  • A continuum part where the states ψE(x) have energy E. We define the density of states ρ(E), such that the number of states with energy in (E,E+dE) is ρ(E)dE.

In this case Z(x,t) can be written as the sum of two contributions: Z(x,t)=(eH^t)0x=nψn(0)ψn*(x)eEnt+0dEρ(E)ψE(0)ψE*(x)eEt.

In absence of disorder, one can find the propagator of the free particle, that, in the original variables, writes: Zfree(x,t)=ex2/(2Tt)2πTt.

Hints: free particle in 1D

For a free particle in one dimension the Hamiltonian is H^=T2x2.

Spectrum. The spectrum is purely continuous. The eigenstates are plane waves ψk(x)=12πeikx,Ek=Tk22, with k. The states are delocalized and satisfy Dirac delta normalization dxψk*(x)ψk(x)=δ(kk).

Energy representation and density of states. For a given energy E>0 there are two degenerate states, ψE±(x)=12πe±i2E/Tx. The density of states is obtained from ρ(E)=dkδ(EEk),Ek=Tk22.

Propagator. Using the spectral decomposition one can write Z(x,t)=0dEρ(E)σ=±ψEσ(0)ψEσ*(x)eEt. Evaluating the resulting Gaussian integral yields Zfree(x,t)=ex2/(2Tt)2πTt.

Useful identity: dxe(ax2+bx)=πaeb2/(4a),a>0.

Cole Hopf Transformation

Replacing

  • T=2ν
  • x=r
  • Z(x,t)=exp(λ2νh(r,t))
  • V(x,t)=λη(r,t)

you get th(r,t)=ν2h(r,t)+λ2(h)2+η(r,t). The KPZ equation!

We can establish a KPZ/Directed polymer dictionary, valid in any dimension. Let us remark that the free energy of the polymer is F=TlnZ(x,t)=1λh(r,t). At low temperature, the free energy approaches the ground state energy Emin.

KPZ / Directed Polymer dictionary
KPZ quantity KPZ scaling Directed polymer quantity Directed polymer scaling
r rt1/z x xtζ
t h(r,t)tα/z t (EminEmin)2t2θ
h h(r,t)rα F,Emin (EminEmin)2t2θ

We conclude that θ=α/z,ζ=1/z. Moreover, the scaling relation θ=2ζ1 is a reincarnation of the Galilean invariance α+z=2.