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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 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.
<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.


=Polymers, interfaces and manifolds in random media=
= Directed Polymers (''d = 1'') =
We consider the following potential energy
<center> <math>
E_{pot}= \int dr \frac{\sigma}{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>  
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.
h(r) </math> is uni-valuated.


=Directed polymers=
Examples: vortex lines, DNA strands, fronts.


==Dijkstra Algorithm and transfer matrix==
Although polymers may form loops, we restrict to directed polymers, i.e., configurations without overhangs or backward turns.


= Directed Polymers on a lattice =


[[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>.]]


[[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>. ]]
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>


We introduce a lattice model for the directed polymer (see figure). In a companion notebook we provide the implementation of the powerful Dijkstra algorithm that allows to identify the minimal  energy among the exponential number of  configurations <math> x(\tau)</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.
<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>.
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>
Note that  <math> \omega= \theta </math>, while for an interface <math> \omega= N \theta </math>. Both  <math> \theta, \zeta </math> are universal. Independent of the lattice, the disorder distribution, the elastic constants, or the boudanry conditions.  


The constants <math> c_\infty,b_\infty, a_\infty </math> are instead non universal. They 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!
<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.


The pdf of the stochastic variables <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:
<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:


* Droplet: <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>)  
* <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>).


* Flat BC: <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>)
* <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>).


==Back to the continuum model, a quantuum approach==
=== Entropy and scaling relation ===


It is useful to re-write the model using the following change of variable
It is useful to compute the entropy
<center> <math>
<math display="block">
h \to x, \quad r\to t
\text{Entropy}= \ln\binom{t}{\frac{t-x}{2}} \approx t \ln 2 -\frac{x^2}{2 t} + O(x^4).
</math></center>
</math>
To fix the idea we can consider polymers of length  <math>
From which one could guess from dimensional analysis
t </math>, starting in  <math>x_0 </math>  and ending in <math>x_t </math>.
<math display="block">
We sum over all possible polymers to compute the partition function at temperature <math>T=1</math>
\theta = 2\zeta - 1.
<center> <math>
</math>
Z[x_t,t ; x_0, 0] =\int_{x(0)=x_0}^{x(t)=x_t} {\cal D} x \exp\left[- \int_0^t d \tau (\partial_\tau x)^2 +V(x,\tau)\right]
This relation is actually exact also for the continuum model.
</math></center>
The previous equation gives the path integral expression of the propagator for a quantum particle, in the imaginary time. Note the the potential is a white noise and thus a time dependent potential.  


In the spirit of the Feyman Kac formula we write the time-dependent Hamiltonian of the particle
= Directed polymers in the continuum =
<center> <math>
\hat H= - \frac{d^2}{d x^2} +V(x,\tau)
</math></center>
Hhe partition function is the solution of the Schrodinger-like equation:
<center> <math>
\partial_t Z =-  \hat H Z = \frac{d^2 Z}{d x^2} - V(x,\tau) Z
</math></center>
The initial condition is  <math>  Z[x_t,t=0 ; x_0, 0]=\delta(x-x_0) </math>.


DISCUTERE CON SATYA LEGAMI FEYMAN KAC FORMULA / transfer matrix
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.


In this equation the noise is multiplicative and not additive as in the previous lecture. However, all KPZ results can be employed today, thanks to  the Cole Hopf transformation.
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.


== Polymer partition function and propagator of a quantum particle ==


== Cole Hopf Transformation==
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
<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.


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.
<math display="block">
Z(x,\tilde t) = \int_{x(0)=0}^{x(\tilde t)=x} \mathcal{D}x(t')\,
\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>


== Dictionary of the Mapping==
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.
 
=== Feynman–Kac formula ===
 
Let's derive the Feynman–Kac formula for <math>Z(x,t)</math> in the general case:
 
* First, focus on free paths and introduce the following probability
<math display="block">
P[A,x,t] = \int_{x(0)=0}^{x(t)=x} \mathcal{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).
</math>
 
* Second, the moment generating function
<math display="block">
Z_p(x,t) = \int_{-\infty}^{\infty} dA\,e^{-pA}P[A,x,t]
= \int_{x(0)=0}^{x(t)=x} \mathcal{D}x(\tau)\,
\exp\!\left[-\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)\right].
</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
<math display="block">
Z_p(x,t+dt)
= Z_p(x,t) + dt\left[\frac{T}{2}\partial_x^2 Z_p - pV(x,t)Z_p\right] + O(dt^2).
</math>
 
Replacing <math>p=1/T</math> we obtain that the partition function is the solution of the Schrödinger-like equation:
<math display="block">
\partial_t Z(x,t)
= -\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>.
 
{| class="wikitable"
|+ KPZ / Directed Polymer dictionary
|-
! KPZ quantity !! KPZ scaling !! Directed polymer quantity !! Directed polymer scaling
|-
| <math>r</math>
| <math>r \sim t^{1/z}</math>
| <math>x</math>
| <math>x \sim t^{\zeta}</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(r,t) \sim r^{\alpha}</math>
| <math>F,\,E_{\min}</math>
| <math>\overline{(E_{\min}-\overline{E_{\min}})^2} \sim t^{2\theta}</math>
|}
 
We conclude that
<math display="block">
\theta = \alpha/z,
\quad
\zeta = 1/z.
</math>
Moreover, the scaling relation <math>\theta = 2\zeta - 1</math> is a reincarnation of the Galilean invariance <math>\alpha + z = 2</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.