introduction to quantum monte carlo
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Introduction to Quantum Monte CarloTRANSCRIPT
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Introduction Introduction to to
Quantum Monte Carlo MethodsQuantum Monte Carlo MethodsClaudio Attaccalite
http://attaccalite.com
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OutlineOutline
A bit of probability theory
Variational Monte Carlo
WaveFunction and Optimization
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Definition of probability
P(Ei)= p
i= Number of successful events
Total Number of experiments
In the limit of a large number of experiments
∑i=1
N
pi=1
joint probability: pi,j
marginal probability:
conditional probability: p(i|j)
probability of composite events
p i=∑k
pi ,k
probability for occurrence of j give that the event i occurred
probability for j whatever the second event may be or not
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More Definitions
Mean Value: ⟨x ⟩=∑i
xipi
The mean value <x> is the expected average value after repeating several times the same experiment
Variance: var x =⟨x2 ⟩−⟨x ⟩2=∑
i
x i−⟨x ⟩ 2pi
The variance is a positive quantity that is zero only if all the events having a nonvanishing probability give the same value for the variable x
i
Standard deviation: =var x
The standard deviation is assumed as a measure of the dispersion of the variable x
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Chebyshev's inequality
P=P [x−⟨x⟩ 2var x
] for ≤1
If the variance is small the random variable x became “more” predictable, in the sense
that is value xi at each event is close to <x> with a
nonnegligible probability
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Extension to Continues Variables
Clearly F(y) is a monotonically increasing function and
And for discrete distributions:
Density probability: y =dF y
dy
Cumulative probability : F y =P {x≤y }
F ∞=1
Obviously: y ≥0
y =∑i
pi y−x Ei
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The law of large number
x=1N∑
i
x i
The average of x is obtained averaging over a large number N of independent realizations of the
same experiment
⟨ x ⟩=⟨x ⟩ var x =⟨ x2 ⟩−⟨ x ⟩=1Nvar x
x =1
2 2/N
e− x−⟨x ⟩
2
22/N
The average of x is Gaussian distributedfor large N and its standard deviation decrease
as 1/sqrt(N)
Central Limit Theorem
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Monte Carlo Example: Estimating π
If you are a very poor dart player, it is easy to imagine throwing darts randomly at the above figure, and it should be apparent that of the total number of darts that hit within the
square, the number of darts that hit the shaded part is proportional to the area of that part.
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In other words:
P inside=r 2
4r2=
4and:
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A Simple Integral
Consider the simple integral:
This can be evaluated in the same way as the pi example. By randomly tossing darts in the interval a-b and evaluating the function f(x) on these points
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The electronic structure problem
P.A.M. Dirac:The fundamental laws necessary for the mathematical treatment of a large part of physics and the whole of chemistry are thus completely known, and the difficulty lies only in the fact that application of these laws leads to equations that are too complex to be solved.
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Monte Carlo integration is necessary because the wavefunction contains explicit particle correlations that
leads to nonfactoring multidimension integrals.
Variational Monte Carlo
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How to sample a given probability distribution?
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Solution Markov chain:random walk in configuration space
xn1=F xn ,n
A Markov chain is a stochastic dynamics for which a random variable x
n evolves according to
xn and x
n+1 are not independent so we can define a
joint probability to go from first to the second
f nxn1 ,xn=K xn1∣xn n xn
Marginal probability to be in xnConditional probability to go from x
n to x
n+1
n1xn1=∑xn
K xn1∣xnn xnMaster equation:
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Limit distributionof the Master equation
n1xn1=∑xn
K xn1∣xnn xn
1) Does It exist a limiting distribution? x
2) Starting form a given arbitrary configuration underwhich condition we converge?
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Sufficient and necessary conditions for the convergence
The answer to the first question requires that:
xn1=∑x
nK xn1∣xn xn
In order to satisfy this requirement it is sufficient but not necessary thatthe socalled detailed balance holds:
K x '∣x x =K x∣x ' x '
The answer to the second question requires ergodicity!
Namely that every configuration x' can be reached in a sufficient large number of Markov interactions,
starting from any initial configuration x
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Nicholas Metropolis (19151999)
The algorithm by Metropolis (and A Rosenbluth, M Rosenbluth, A Teller and E Teller, 1953) has been cited as among the top 10 algorithms having the "greatest influence on the development and practice of science and engineering in the 20th century."
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Metropolis AlgorithmWe want
1) a Markov chain such that, for large n, converge to (x)2) a condition probability K(x'|x) that satisfy the detailed balance with this
probability distribution
Solution! (by Metropolis and friends)
K x '∣x=Ax '∣x T x '∣x
Ax'∣x=min{1, x'T x∣x 'xT x '∣x }
where T(x'|x) is a general and reasonable transition probability from x to x'
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The Algorithmstart from a random configuration x'
generate a new one according to T(x'|x)
accept or reject according to Metropolis rule
evaluate our function
ImportantIt not necessary to have a normalized probability
distribution (or wavefunction!)
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More or less we have arrivedwe can evaluate this integral
but we just need a wave function . . . . . . .
⟨ A⟩=∫ R A R dR
∫ R2dR
=∫ AL R
2RdR
∫ R2dR
and its variance
var A=∫ AL
2 R 2R dR
∫ R2dR−⟨ A⟩
2
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The trialfunction completely determinesquality of the approximation for the physical observables
r1,r2,. .. , rn=Det∣A∣expUcorr
The simplest WF is the SlaterJastrow
The trial wavefunction
other functional forms: pairing BCS, multideterminant, pfaffian
Det from DFT, CI, HF, scratch, etc..
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Optimization strategies
EV a ,b,c =∫ a ,b,c..H a ,b,c...dRn
∫ ¿
The Variational Energy
The Variance of the Energy:
(always positive and 0 for exact ground state!) 2a ,b,c...=∫ [
H
]2
2−Ev
2
In order to obtain a good variational wavefunction, it is possible to optimize the WF minimizing one of the following functionals
or a linear combination of both
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And finally an application!!!
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2D electron gas
Unpolarized phase Unpolarized phase Wigner Crystal
H=−12r s
2∑i
N
∇ i21r s
∑i j
N 1∣r i−r j∣
rS=1
naB
The Hamiltonian :
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2D electron gas: the phase diagram
We found a new phase of the 2D electron gas at
low density a stable spin polarized
phase before the Wigner
crystallization.
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Difficulties With VMC
The manyelectron wavefunction is unknownHas to be approximatedMay seem hopeless to have to actually guess
the wavefunctionBut is surprisingly accurate when it works
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The Limitation of VMC
Nothing can really be done if the trial wavefunction isn’t accurate enough
Moreover it favours simple states over more complicated ones
Therefore, there are other methodsExample: Diffusion QMC
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Diffusion Monte Carlo and SignProblem
Applications
Then . . . . Finite Temperature PathIntegral Monte Carlo
Onedimensional electron gas
Excited States
Onebody density matrix
Diagramatic Monte Carlo
Next Monday
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Reference
SISSA Lectures on Numerical methods for strongly correlated electrons 4th draft S. Sorella G. E. Santoro and F. Becca (2008)
Introduction to Diffusion Monte Carlo MethodI. Kostin, B. Faber and K. Schulten, physics/9702023v1 (1995)
FreeScience.info> Quantum Monte Carlohttp://www.freescience.info/books.php?id=35
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Exact conditionsElectronNuclei cusp conditions
The same condition holds when two electron meet electronelectron cusp condition and can be satisfied with a
twobody Jastrow factor
When one electron approach a nuclei the wavefunction reduceto a simple hydrogen like, namely: