qmc-lttc/QMC.org

#+TITLE: Quantum Monte Carlo
#+AUTHOR: Anthony Scemama, Claudia Filippi
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  #+RESULTS:
  : /home/scemama/TREX/qmc-lttc/QMC.pdf

* Introduction

  We propose different exercises to understand quantum Monte Carlo (QMC)
  methods. In the first section, we propose to compute the energy of a
  hydrogen atom using numerical integration. The goal of this section is
  to introduce the /local energy/.
  Then we introduce the variational Monte Carlo (VMC) method which
  computes a statistical estimate of the expectation value of the energy
  associated with a given wave function.
  Finally, we introduce the diffusion Monte Carlo (DMC) method which
  gives the exact energy of the $H_2$ molecule. 

  Code examples will be given in Python and Fortran. Whatever language
  can be chosen.

  We consider the stationary solution of the Schrödinger equation, so
  the wave functions considered here are real: for an $N$ electron
  system where the electrons move in the 3-dimensional space,
  $\Psi : \mathbb{R}^{3N} \rightarrow \mathbb{R}$. In addition, $\Psi$
  is defined everywhere, continuous and infinitely differentiable.
  
 *Note*
  #+begin_important
  In Fortran, when  you use a double precision  constant, don't forget
  to  put ~d0~ as  a  suffix (for example ~2.0d0~),  or  it will  be
  interpreted as a single precision value
  #+end_important


* Numerical evaluation of the energy

  In this section we consider the Hydrogen atom with the following
  wave function:

  $$
  \Psi(\mathbf{r}) = \exp(-a |\mathbf{r}|)
  $$

  We will first verify that, for a given value of $a$, $\Psi$ is an
  eigenfunction of the Hamiltonian

  $$
  \hat{H} = \hat{T} + \hat{V} = - \frac{1}{2} \Delta - \frac{1}{|\mathbf{r}|}
  $$

  To do that, we will check if the local energy, defined as

  $$
  E_L(\mathbf{r}) = \frac{\hat{H} \Psi(\mathbf{r})}{\Psi(\mathbf{r})},
  $$

  is constant.


  The probabilistic /expected value/ of an arbitrary function $f(x)$
  with respect to a probability density function $p(x)$ is given by

  $$ \langle f \rangle_p = \int_{-\infty}^\infty p(x)\, f(x)\,dx. $$

  Recall that a probability density function $p(x)$ is non-negative
  and integrates to one:

  $$ \int_{-\infty}^\infty p(x)\,dx = 1. $$

    
  The electronic energy of a system is the expectation value of the
  local energy $E(\mathbf{r})$ with respect to the 3N-dimensional
  electron density given by the square of the wave function:

  \begin{eqnarray*}
  E & = & \frac{\langle \Psi| \hat{H} | \Psi\rangle}{\langle \Psi |\Psi \rangle} 
      =   \frac{\int \Psi(\mathbf{r})\, \hat{H} \Psi(\mathbf{r})\, d\mathbf{r}}{\int \left[\Psi(\mathbf{r}) \right]^2 d\mathbf{r}} \\
    & = & \frac{\int \left[\Psi(\mathbf{r})\right]^2\, \frac{\hat{H} \Psi(\mathbf{r})}{\Psi(\mathbf{r})}\,d\mathbf{r}}{\int \left[\Psi(\mathbf{r}) \right]^2 d\mathbf{r}} 
      =   \frac{\int \left[\Psi(\mathbf{r})\right]^2\, E_L(\mathbf{r})\,d\mathbf{r}}{\int \left[\Psi(\mathbf{r}) \right]^2 d\mathbf{r}} 
      =   \langle E_L \rangle_{\Psi^2}
  \end{eqnarray*}

** Local energy
   :PROPERTIES:
   :header-args:python: :tangle hydrogen.py
   :header-args:f90: :tangle hydrogen.f90
   :END:

   Write all the functions of this section in a single file :
 ~hydrogen.py~ if you use Python, or ~hydrogen.f90~ is you use
   Fortran.
   
*** Exercise 1

    #+begin_exercise
    Find the theoretical value of $a$ for which $\Psi$ is an eigenfunction of $\hat{H}$.
    #+end_exercise

*** Exercise 2

    #+begin_exercise
    Write a function which computes the potential at $\mathbf{r}$.
    The function accepts a 3-dimensional vector =r= as input arguments
    and returns the potential.
    #+end_exercise

    $\mathbf{r}=\left( \begin{array}{c} x \\ y\\ z\end{array} \right)$, so
    $$
    V(\mathbf{r}) = -\frac{1}{\sqrt{x^2 + y^2 + z^2}}
    $$

**** Python
     #+BEGIN_SRC python :results none :tangle none
import numpy as np

def potential(r):
    # TODO
     #+END_SRC

**** Python                                                        :solution:
     #+BEGIN_SRC python :results none
import numpy as np

def potential(r):
    return -1. / np.sqrt(np.dot(r,r))
     #+END_SRC

**** Fortran 
     #+BEGIN_SRC f90 :tangle none
double precision function potential(r)
  implicit none
  double precision, intent(in) :: r(3)
  ! TODO
end function potential
     #+END_SRC

**** Fortran                                                       :solution:
     #+BEGIN_SRC f90 
double precision function potential(r)
  implicit none
  double precision, intent(in) :: r(3)
  potential = -1.d0 / dsqrt( r(1)*r(1) + r(2)*r(2) + r(3)*r(3) )
end function potential
     #+END_SRC

*** Exercise 3 
    #+begin_exercise
    Write a function which computes the wave function at $\mathbf{r}$.
    The function accepts a scalar =a= and a 3-dimensional vector =r= as
    input arguments, and returns a scalar.
    #+end_exercise

    
**** Python
     #+BEGIN_SRC python :results none
def psi(a, r):
    # TODO
     #+END_SRC

**** Python                                                        :solution:
     #+BEGIN_SRC python :results none
def psi(a, r):
    return np.exp(-a*np.sqrt(np.dot(r,r)))
     #+END_SRC

**** Fortran
     #+BEGIN_SRC f90 
double precision function psi(a, r)
  implicit none
  double precision, intent(in) :: a, r(3)
  ! TODO
end function psi
     #+END_SRC
     
**** Fortran                                                       :solution:
     #+BEGIN_SRC f90 
double precision function psi(a, r)
  implicit none
  double precision, intent(in) :: a, r(3)
  psi = dexp(-a * dsqrt( r(1)*r(1) + r(2)*r(2) + r(3)*r(3) ))
end function psi
     #+END_SRC
     
*** Exercise 4
    #+begin_exercise
    Write a function which computes the local kinetic energy at $\mathbf{r}$.
    The function accepts =a= and =r= as input arguments and returns the
    local kinetic energy.
    #+end_exercise

    The local kinetic energy is defined as $$-\frac{1}{2}\frac{\Delta \Psi}{\Psi}.$$
     
    We differentiate $\Psi$ with respect to $x$:
     
    \[\Psi(\mathbf{r})  =  \exp(-a\,|\mathbf{r}|) \]
    \[\frac{\partial \Psi}{\partial x}
      = \frac{\partial \Psi}{\partial |\mathbf{r}|} \frac{\partial |\mathbf{r}|}{\partial x}   
      =  - \frac{a\,x}{|\mathbf{r}|} \Psi(\mathbf{r}) \]

    and we differentiate a second time:

    $$
    \frac{\partial^2 \Psi}{\partial x^2} =
    \left( \frac{a^2\,x^2}{|\mathbf{r}|^2}  -
    \frac{a(y^2+z^2)}{|\mathbf{r}|^{3}} \right) \Psi(\mathbf{r}).
    $$

    The Laplacian operator $\Delta = \frac{\partial^2}{\partial x^2} +
    \frac{\partial^2}{\partial y^2} + \frac{\partial^2}{\partial z^2}$
    applied to the wave function gives:

    $$
    \Delta \Psi (\mathbf{r}) = \left(a^2 - \frac{2a}{\mathbf{|r|}} \right) \Psi(\mathbf{r})
    $$

    So the local kinetic energy is
    $$
    -\frac{1}{2} \frac{\Delta \Psi}{\Psi} (\mathbf{r}) = -\frac{1}{2}\left(a^2 - \frac{2a}{\mathbf{|r|}} \right) 
    $$
     
**** Python
     #+BEGIN_SRC python :results none
def kinetic(a,r):
    # TODO
     #+END_SRC

**** Python                                                        :solution:
     #+BEGIN_SRC python :results none
def kinetic(a,r):
    return -0.5 * (a**2 - (2.*a)/np.sqrt(np.dot(r,r)))
     #+END_SRC

**** Fortran
     #+BEGIN_SRC f90 
double precision function kinetic(a,r)
  implicit none
  double precision, intent(in) :: a, r(3)
  ! TODO
end function kinetic
     #+END_SRC

**** Fortran                                                       :solution:
     #+BEGIN_SRC f90 
double precision function kinetic(a,r)
  implicit none
  double precision, intent(in) :: a, r(3)
  kinetic = -0.5d0 * (a*a - (2.d0*a) / &
       dsqrt( r(1)*r(1) + r(2)*r(2) + r(3)*r(3) ) ) 
end function kinetic
     #+END_SRC

*** Exercise 5
    #+begin_exercise
    Write a function which computes the local energy at $\mathbf{r}$,
    using the previously defined functions.
    The function accepts =a= and =r= as input arguments and returns the
    local kinetic energy.
    #+end_exercise
   
    $$
    E_L(\mathbf{r}) = -\frac{1}{2} \frac{\Delta \Psi}{\Psi} (\mathbf{r}) + V(\mathbf{r})
    $$

    
**** Python
     #+BEGIN_SRC python :results none
def e_loc(a,r):
    #TODO
     #+END_SRC

**** Python                                                        :solution:
     #+BEGIN_SRC python :results none
def e_loc(a,r):
    return kinetic(a,r) + potential(r)
     #+END_SRC

**** Fortran
     #+BEGIN_SRC f90
double precision function e_loc(a,r)
  implicit none
  double precision, intent(in) :: a, r(3)
  ! TODO
end function e_loc
     #+END_SRC
   
**** Fortran                                                       :solution:
     #+BEGIN_SRC f90
double precision function e_loc(a,r)
  implicit none
  double precision, intent(in) :: a, r(3)
  double precision, external   :: kinetic, potential
  e_loc = kinetic(a,r) + potential(r)
end function e_loc
     #+END_SRC
   
** Plot of the local energy along the $x$ axis
   :PROPERTIES:
   :header-args:python: :tangle plot_hydrogen.py
   :header-args:f90: :tangle plot_hydrogen.f90
   :END:
   
*** Exercise
    #+begin_exercise
    For multiple values of $a$ (0.1, 0.2, 0.5, 1., 1.5, 2.), plot the
    local energy along the $x$ axis. In Python, you can use matplotlib
    for example. In Fortran, it is convenient to write in a text file
    the values of $x$ and $E_L(\mathbf{r})$ for each point, and use
    Gnuplot to plot the files.
    #+end_exercise

**** Python
     #+BEGIN_SRC python :results none
import numpy as np
import matplotlib.pyplot as plt

from hydrogen import e_loc

x=np.linspace(-5,5)
plt.figure(figsize=(10,5))

# TODO

plt.tight_layout()
plt.legend()
plt.savefig("plot_py.png")
     #+end_src

**** Python                                                        :solution:
     #+BEGIN_SRC python :results none
import numpy as np
import matplotlib.pyplot as plt

from hydrogen import e_loc

x=np.linspace(-5,5)
plt.figure(figsize=(10,5))

for a in [0.1, 0.2, 0.5, 1., 1.5, 2.]:
  y=np.array([ e_loc(a, np.array([t,0.,0.]) ) for t in x])
  plt.plot(x,y,label=f"a={a}")

plt.tight_layout()
plt.legend()
plt.savefig("plot_py.png")
     #+end_src

     #+RESULTS:

     [[./plot_py.png]]

**** Fortran
        #+begin_src f90 
program plot
  implicit none
  double precision, external :: e_loc

  double precision :: x(50), dx
  integer :: i, j

  dx = 10.d0/(size(x)-1)
  do i=1,size(x)
     x(i) = -5.d0 + (i-1)*dx
  end do

  ! TODO

end program plot
        #+end_src

        To compile and run:

        #+begin_src sh :exports both
gfortran hydrogen.f90 plot_hydrogen.f90 -o plot_hydrogen
./plot_hydrogen > data
        #+end_src

        To plot the data using gnuplot:

        #+begin_src gnuplot :file plot.png :exports both
set grid
set xrange [-5:5]
set yrange [-2:1]
plot './data' index 0 using 1:2 with lines title 'a=0.1', \
     './data' index 1 using 1:2 with lines title 'a=0.2', \
     './data' index 2 using 1:2 with lines title 'a=0.5', \
     './data' index 3 using 1:2 with lines title 'a=1.0', \
     './data' index 4 using 1:2 with lines title 'a=1.5', \
     './data' index 5 using 1:2 with lines title 'a=2.0'
        #+end_src

**** Fortran                                                       :solution:
        #+begin_src f90 
program plot
  implicit none
  double precision, external :: e_loc

  double precision :: x(50), energy, dx, r(3), a(6)
  integer :: i, j

  a = (/ 0.1d0, 0.2d0, 0.5d0, 1.d0, 1.5d0, 2.d0 /)

  dx = 10.d0/(size(x)-1)
  do i=1,size(x)
     x(i) = -5.d0 + (i-1)*dx
  end do

  r(:) = 0.d0

  do j=1,size(a)
     print *, '# a=', a(j)
     do i=1,size(x)
        r(1) = x(i)
        energy = e_loc( a(j), r )
        print *, x(i), energy
     end do
     print *, ''
     print *, ''
  end do

end program plot
        #+end_src

        To compile and run:

        #+begin_src sh :exports both
gfortran hydrogen.f90 plot_hydrogen.f90 -o plot_hydrogen
./plot_hydrogen > data
        #+end_src

        #+RESULTS:

        To plot the data using gnuplot:

        #+begin_src gnuplot :file plot.png :exports both
set grid
set xrange [-5:5]
set yrange [-2:1]
plot './data' index 0 using 1:2 with lines title 'a=0.1', \
     './data' index 1 using 1:2 with lines title 'a=0.2', \
     './data' index 2 using 1:2 with lines title 'a=0.5', \
     './data' index 3 using 1:2 with lines title 'a=1.0', \
     './data' index 4 using 1:2 with lines title 'a=1.5', \
     './data' index 5 using 1:2 with lines title 'a=2.0'
        #+end_src

        #+RESULTS:
        [[file:plot.png]]

** TODO Numerical estimation of the energy
   :PROPERTIES:
   :header-args:python: :tangle energy_hydrogen.py
   :header-args:f90: :tangle energy_hydrogen.f90
   :END:

   If the space is discretized in small volume elements $\mathbf{r}_i$
   of size $\delta \mathbf{r}$, the expression of $\langle E_L \rangle_{\Psi^2}$
   becomes a weighted average of the local energy, where the weights
   are the values of the probability density at $\mathbf{r}_i$
   multiplied by the volume element:
     
   $$
   \langle E \rangle_{\Psi^2} \approx \frac{\sum_i w_i E_L(\mathbf{r}_i)}{\sum_i w_i}, \;\;
   w_i = \left[\Psi(\mathbf{r}_i)\right]^2 \delta \mathbf{r}
   $$
     
   #+begin_note
   The energy is biased because:
   - The volume elements are not infinitely small (discretization error)
   - The energy is evaluated only inside the box (incompleteness of the space)
   #+end_note

   
*** Exercise
     #+begin_exercise
    Compute a numerical estimate of the energy in a grid of
    $50\times50\times50$ points in the range $(-5,-5,-5) \le
    \mathbf{r} \le (5,5,5)$.
     #+end_exercise

 *Python*
      #+BEGIN_SRC python :results none
import numpy as np
from hydrogen import e_loc, psi

interval = np.linspace(-5,5,num=50)
delta = (interval[1]-interval[0])**3

r = np.array([0.,0.,0.])

for a in [0.1, 0.2, 0.5, 0.9, 1., 1.5, 2.]:
    E = 0.
    norm = 0.
      for x in interval:
          r[0] = x
            for y in interval:
                r[1] = y
                  for z in interval:
                      r[2] = z
                      w = psi(a,r)
                      w = w * w * delta
                      E    += w * e_loc(a,r)
                      norm += w 
    E = E / norm
    print(f"a = {a} \t E = {E}")                

      #+end_src

      #+RESULTS:
      : a = 0.1 	 E = -0.24518438948809218
      : a = 0.2 	 E = -0.26966057967803525
      : a = 0.5 	 E = -0.3856357612517407
      : a = 0.9 	 E = -0.49435709786716214
      : a = 1.0 	 E = -0.5
      : a = 1.5 	 E = -0.39242967082602226
      : a = 2.0 	 E = -0.08086980667844901

 *Fortran*
      #+begin_src f90 
program energy_hydrogen
  implicit none
  double precision, external :: e_loc, psi
  double precision :: x(50), w, delta, energy, dx, r(3), a(6), norm
  integer :: i, k, l, j

  a = (/ 0.1d0, 0.2d0, 0.5d0, 1.d0, 1.5d0, 2.d0 /)

  dx = 10.d0/(size(x)-1)
  do i=1,size(x)
     x(i) = -5.d0 + (i-1)*dx
  end do

  delta = dx**3

  r(:) = 0.d0

  do j=1,size(a)
     energy = 0.d0
     norm = 0.d0
     do i=1,size(x)
        r(1) = x(i)
        do k=1,size(x)
           r(2) = x(k)
           do l=1,size(x)
              r(3) = x(l)
              w = psi(a(j),r)
              w = w * w * delta
              energy = energy + w * e_loc(a(j), r)
              norm   = norm   + w 
           end do
        end do
     end do
     energy = energy / norm
     print *, 'a = ', a(j), '    E = ', energy
  end do

end program energy_hydrogen
      #+end_src

      To compile the Fortran and run it:

      #+begin_src sh :results output :exports both
gfortran hydrogen.f90 energy_hydrogen.f90 -o energy_hydrogen
./energy_hydrogen 
      #+end_src

      #+RESULTS:
      :  a =   0.10000000000000001          E =  -0.24518438948809140     
      :  a =   0.20000000000000001          E =  -0.26966057967803236     
      :  a =   0.50000000000000000          E =  -0.38563576125173815     
      :  a =    1.0000000000000000          E =  -0.50000000000000000     
      :  a =    1.5000000000000000          E =  -0.39242967082602065     
      :  a =    2.0000000000000000          E =   -8.0869806678448772E-002

** TODO Variance of the local energy
   :PROPERTIES:
   :header-args:python: :tangle variance_hydrogen.py
   :header-args:f90: :tangle variance_hydrogen.f90
   :END:

   The variance of the local energy is a functional of $\Psi$
   which measures the magnitude of the fluctuations of the local
   energy associated with $\Psi$ around the average:

   $$
   \sigma^2(E_L) = \frac{\int \left[\Psi(\mathbf{r})\right]^2\, \left[
   E_L(\mathbf{r}) - E \right]^2 \, d\mathbf{r}}{\int \left[\Psi(\mathbf{r}) \right]^2 d\mathbf{r}}
   $$
   which can be simplified as
   
   $$ \sigma^2(E_L) = \langle E_L^2 \rangle - \langle E_L \rangle^2 $$

   If the local energy is constant (i.e. $\Psi$ is an eigenfunction of
   $\hat{H}$) the variance is zero, so the variance of the local
   energy can be used as a measure of the quality of a wave function.

*** Exercise (optional)
   #+begin_exercise
   Prove that :
   $$\langle E - \langle E \rangle \rangle^2 = \langle E^2 \rangle - \langle E \rangle^2 $$
   #+end_exercise
   
*** Exercise
   #+begin_exercise
   Add the calculation of the variance to the previous code, and 
   compute a numerical estimate of the variance of the local energy
   in a grid of $50\times50\times50$ points in the range
   $(-5,-5,-5)
   \le \mathbf{r} \le (5,5,5)$ for different values of $a$.
   #+end_exercise
     
 *Python*
   #+begin_src python :results none
import numpy as np
from hydrogen import e_loc, psi

interval = np.linspace(-5,5,num=50)
delta = (interval[1]-interval[0])**3

r = np.array([0.,0.,0.])

for a in [0.1, 0.2, 0.5, 0.9, 1., 1.5, 2.]:
    E = 0.
    E2 = 0.
    norm = 0.
    for x in interval:
        r[0] = x
        for y in interval:
            r[1] = y
            for z in interval:
                r[2] = z
                w = psi(a, r)
                w = w * w * delta
                El = e_loc(a, r)
                E  += w * El
                E2 += w * El*El
                norm += w 
    E = E / norm
    E2 = E2 / norm
    s2 = E2 - E*E
    print(f"a = {a} \t E = {E:10.8f}  \t  \sigma^2 = {s2:10.8f}")
   #+end_src

   #+RESULTS:
   : a = 0.1 	 E = -0.24518439  	  \sigma^2 = 0.02696522
   : a = 0.2 	 E = -0.26966058  	  \sigma^2 = 0.03719707
   : a = 0.5 	 E = -0.38563576  	  \sigma^2 = 0.05318597
   : a = 0.9 	 E = -0.49435710  	  \sigma^2 = 0.00577812
   : a = 1.0 	 E = -0.50000000  	  \sigma^2 = 0.00000000
   : a = 1.5 	 E = -0.39242967  	  \sigma^2 = 0.31449671
   : a = 2.0 	 E = -0.08086981  	  \sigma^2 = 1.80688143

 *Fortran*
   #+begin_src f90 
program variance_hydrogen
  implicit none
  double precision, external :: e_loc, psi
  double precision :: x(50), w, delta, energy, dx, r(3), a(6), norm, s2
  double precision :: e, energy2
  integer :: i, k, l, j

  a = (/ 0.1d0, 0.2d0, 0.5d0, 1.d0, 1.5d0, 2.d0 /)

  dx = 10.d0/(size(x)-1)
  do i=1,size(x)
     x(i) = -5.d0 + (i-1)*dx
  end do

  delta = dx**3

  r(:) = 0.d0

  do j=1,size(a)
     energy = 0.d0
     energy2 = 0.d0
     norm = 0.d0
     do i=1,size(x)
        r(1) = x(i)
        do k=1,size(x)
           r(2) = x(k)
           do l=1,size(x)
              r(3) = x(l)
              w = psi(a(j),r)
              w = w * w * delta
              e = e_loc(a(j), r)
              energy  = energy  + w * e
              energy2 = energy2 + w * e * e
              norm   = norm   + w 
           end do
        end do
     end do
     energy  = energy  / norm
     energy2 = energy2 / norm
     s2 = energy2 - energy*energy
     print *, 'a = ', a(j), ' E = ', energy, ' s2 = ', s2
  end do

end program variance_hydrogen
   #+end_src

   To compile and run:

   #+begin_src sh :results output :exports both
gfortran hydrogen.f90 variance_hydrogen.f90 -o variance_hydrogen
./variance_hydrogen 
   #+end_src

   #+RESULTS:
   :  a =   0.10000000000000001       E =  -0.24518438948809140       s2 =    2.6965218719722767E-002
   :  a =   0.20000000000000001       E =  -0.26966057967803236       s2 =    3.7197072370201284E-002
   :  a =   0.50000000000000000       E =  -0.38563576125173815       s2 =    5.3185967578480653E-002
   :  a =    1.0000000000000000       E =  -0.50000000000000000       s2 =    0.0000000000000000     
   :  a =    1.5000000000000000       E =  -0.39242967082602065       s2 =   0.31449670909172917     
   :  a =    2.0000000000000000       E =   -8.0869806678448772E-002  s2 =    1.8068814270846534     


* TODO Variational Monte Carlo

  Numerical integration with deterministic methods is very efficient
  in low dimensions. When the number of dimensions becomes large,
  instead of computing the average energy as a numerical integration
  on a grid, it is usually more efficient to do a Monte Carlo sampling.

  Moreover, a Monte Carlo sampling will alow us to remove the bias due
  to the discretization of space, and compute a statistical confidence
  interval.

** TODO Computation of the statistical error
   :PROPERTIES:
   :header-args:python: :tangle qmc_stats.py
   :header-args:f90: :tangle qmc_stats.f90
   :END:

   To compute the statistical error, you need to perform $M$
   independent Monte Carlo calculations. You will obtain $M$ different
   estimates of the energy, which are expected to have a Gaussian
   distribution by the central limit theorem.

   The estimate of the energy is

   $$
   E = \frac{1}{M} \sum_{i=1}^M E_M
   $$

   The variance of the average energies can be computed as

   $$
   \sigma^2 = \frac{1}{M-1} \sum_{i=1}^{M} (E_M - E)^2
   $$

   And the confidence interval is given by

   $$
   E \pm \delta E, \text{ where } \delta E = \frac{\sigma}{\sqrt{M}}
   $$
   
*** Exercise
   #+begin_exercise
   Write a function returning the average and statistical error of an
   input array.
   #+end_exercise

 *Python*
   #+BEGIN_SRC python :results none
from math import sqrt
def ave_error(arr):
    M = len(arr)
    assert (M>1)
    average = sum(arr)/M
    variance = 1./(M-1) * sum( [ (x - average)**2 for x in arr ] )
    return (average, sqrt(variance/M))
   #+END_SRC

 *Fortran*
   #+BEGIN_SRC f90
subroutine ave_error(x,n,ave,err)
  implicit none
  integer, intent(in)           :: n 
  double precision, intent(in)  :: x(n) 
  double precision, intent(out) :: ave, err
  double precision :: variance
  if (n == 1) then
     ave = x(1)
     err = 0.d0
  else
     ave = sum(x(:)) / dble(n)
     variance = sum( (x(:) - ave)**2 ) / dble(n-1)
     err = dsqrt(variance/dble(n))
  endif
end subroutine ave_error
   #+END_SRC
   
** TODO Uniform sampling in the box
   :PROPERTIES:
   :header-args:python: :tangle qmc_uniform.py
   :header-args:f90: :tangle qmc_uniform.f90
   :END:

   We will now do our first Monte Carlo calculation to compute the
   energy of the hydrogen atom.
   
   At every Monte Carlo step:

   - Draw a random point $\mathbf{r}_i$ in the box $(-5,-5,-5) \le
     (x,y,z) \le (5,5,5)$
   - Compute $[\Psi(\mathbf{r}_i)]^2$ and accumulate the result in a
     variable =normalization=
   - Compute $[\Psi(\mathbf{r}_i)]^2 \times E_L(\mathbf{r}_i)$, and accumulate the
     result in a variable =energy=

   One Monte Carlo run will consist of $N$ Monte Carlo steps. Once all the
   steps have been computed, the run returns the average energy
   $\bar{E}_k$ over the $N$ steps of the run.

   To compute the statistical error, perform $M$ runs. The final
   estimate of the energy will be the average over the $\bar{E}_k$,
   and the variance of the $\bar{E}_k$ will be used to compute the
   statistical error.
   
*** Exercise

    #+begin_exercise
    Parameterize the wave function with $a=0.9$.  Perform 30
    independent Monte Carlo runs, each with 100 000 Monte Carlo
    steps. Store the final energies of each run and use this array to
    compute the average energy and the associated error bar.
    #+end_exercise

 *Python*
    #+BEGIN_SRC python :results output
from hydrogen  import *
from qmc_stats import *

def MonteCarlo(a, nmax):
     E = 0.
     N = 0.
     for istep in range(nmax):
          r = np.random.uniform(-5., 5., (3))
          w = psi(a,r)
          w = w*w
          N += w
          E += w * e_loc(a,r)
   return E/N

a = 0.9
nmax = 100000
X = [MonteCarlo(a,nmax) for i in range(30)]
E, deltaE = ave_error(X)
print(f"E = {E} +/- {deltaE}")
    #+END_SRC

    #+RESULTS:
    : E = -0.4956255109300764 +/- 0.0007082875482711226

 *Fortran*
#+begin_note
When running Monte Carlo calculations, the number of steps is
usually very large. We expect =nmax= to be possibly larger than 2
billion, so we use 8-byte integers (=integer*8=) to represent it, as
well as the index of the current step.
#+end_note

    #+BEGIN_SRC f90
subroutine uniform_montecarlo(a,nmax,energy)
  implicit none
  double precision, intent(in)  :: a
  integer*8       , intent(in)  :: nmax 
  double precision, intent(out) :: energy

  integer*8 :: istep

  double precision :: norm, r(3), w

  double precision, external :: e_loc, psi

  energy = 0.d0
  norm   = 0.d0
  do istep = 1,nmax
     call random_number(r)
     r(:) = -5.d0 + 10.d0*r(:)
     w = psi(a,r)
     w = w*w
     norm = norm + w
     energy = energy + w * e_loc(a,r)
  end do
  energy = energy / norm
end subroutine uniform_montecarlo

program qmc
  implicit none
  double precision, parameter :: a = 0.9
  integer*8       , parameter :: nmax = 100000
  integer         , parameter :: nruns = 30

  integer :: irun
  double precision :: X(nruns)
  double precision :: ave, err

  do irun=1,nruns
     call uniform_montecarlo(a,nmax,X(irun))
  enddo
  call ave_error(X,nruns,ave,err)
  print *, 'E = ', ave, '+/-', err
end program qmc
    #+END_SRC

    #+begin_src sh :results output :exports both
gfortran hydrogen.f90 qmc_stats.f90 qmc_uniform.f90 -o qmc_uniform
./qmc_uniform
    #+end_src

    #+RESULTS:
    :  E =  -0.49588321986667677      +/-   7.1758863546737969E-004

** TODO Metropolis sampling with $\Psi^2$
   :PROPERTIES:
   :header-args:python: :tangle qmc_metropolis.py
   :header-args:f90: :tangle qmc_metropolis.f90
   :END:

   We will now use the square of the wave function to sample random
   points distributed with the probability density
   \[
   P(\mathbf{r}) = \left[\Psi(\mathbf{r})\right]^2
   \]

   The expression of the average energy is now simplified to the average of
   the local energies, since the weights are taken care of by the
   sampling :

   $$
   E \approx \frac{1}{M}\sum_{i=1}^M E_L(\mathbf{r}_i)
   $$
   

   To sample a chosen probability density, an efficient method is the 
   [[https://en.wikipedia.org/wiki/Metropolis%E2%80%93Hastings_algorithm][Metropolis-Hastings sampling algorithm]]. Starting from a random
   initial position $\mathbf{r}_0$, we will realize a random walk as follows:

   $$
   \mathbf{r}_{n+1} = \mathbf{r}_{n} + \tau \mathbf{u}
   $$

   where $\tau$ is a fixed constant (the so-called /time-step/), and
   $\mathbf{u}$ is a uniform random number in a 3-dimensional box
   $(-1,-1,-1) \le \mathbf{u} \le (1,1,1)$. We will then add the
   accept/reject step that will guarantee that the distribution of the
   $\mathbf{r}_n$ is $\Psi^2$:

   - Compute a new position $\mathbf{r}_{n+1}$
   - Draw a uniform random number $v \in [0,1]$
   - Compute the ratio $R = \frac{\left[\Psi(\mathbf{r}_{n+1})\right]^2}{\left[\Psi(\mathbf{r}_{n})\right]^2}$
   - if $v \le R$, accept the move (do nothing)
   - else, reject the move (set $\mathbf{r}_{n+1} = \mathbf{r}_n$)
   - evaluate the local energy at $\mathbf{r}_{n+1}$ 
   
   #+begin_note
    A common error is to remove the rejected samples from the
    calculation of the average. *Don't do it!*

    All samples should be kept, from both accepted and rejected moves.
   #+end_note
   
   If the time step is infinitely small, the ratio will be very close
   to one and all the steps will be accepted. But the trajectory will
   be infinitely too short to have statistical significance.

   On the other hand, as the time step increases, the number of
   accepted steps will decrease because the ratios might become
   small. If the number of accepted steps is close to zero, then the
   space is not well sampled either.

   The time step should be adjusted so that it is as large as
   possible, keeping the number of accepted steps not too small. To
   achieve that we define the acceptance rate as the number of
   accepted steps over the total number of steps. Adjusting the time
   step such that the acceptance rate is close to 0.5 is a good compromise.
   
   
*** Exercise
    
    #+begin_exercise
    Modify the program of the previous section to compute the energy, sampling with
    $Psi^2$.
    Compute also the acceptance rate, so that you can adapt the time
    step in order to have an acceptance rate close to 0.5 .
    Can you observe a reduction in the statistical error?
    #+end_exercise

 *Python*
    #+BEGIN_SRC python :results output
from hydrogen  import *
from qmc_stats import *

def MonteCarlo(a,nmax,tau):
    E = 0.
    N = 0.
    N_accep = 0.
    r_old = np.random.uniform(-tau, tau, (3))
    psi_old = psi(a,r_old)
    for istep in range(nmax):
        r_new = r_old + np.random.uniform(-tau,tau,(3))
        psi_new = psi(a,r_new)
        ratio = (psi_new / psi_old)**2
        v = np.random.uniform(0,1,(1))
        if v < ratio:
            N_accep += 1.
            r_old = r_new
            psi_old = psi_new
        N += 1.
        E += e_loc(a,r_old)
    return E/N, N_accep/N

a = 0.9
nmax = 100000
tau = 1.3
X0 = [ MonteCarlo(a,nmax,tau) for i in range(30)]
X = [ x for x, _ in X0 ]
A = [ x for _, x in X0 ]
E, deltaE = ave_error(X)
A, deltaA = ave_error(A)
print(f"E = {E} +/- {deltaE}")
print(f"A = {A} +/- {deltaA}")
    #+END_SRC

    #+RESULTS:
    : E = -0.4950720838131573 +/- 0.00019089638602238043
    : A = 0.5172960000000001 +/- 0.0003443446549306529

 *Fortran*
    #+BEGIN_SRC f90
subroutine metropolis_montecarlo(a,nmax,tau,energy,accep)
  implicit none
  double precision, intent(in)  :: a
  integer*8       , intent(in)  :: nmax 
  double precision, intent(in)  :: tau
  double precision, intent(out) :: energy
  double precision, intent(out) :: accep

  integer*8 :: istep

  double precision :: norm, r_old(3), r_new(3), psi_old, psi_new
  double precision :: v, ratio, n_accep
  double precision, external :: e_loc, psi, gaussian

  energy = 0.d0
  norm   = 0.d0
  n_accep = 0.d0
  call random_number(r_old)
  r_old(:) = tau * (2.d0*r_old(:) - 1.d0)
  psi_old = psi(a,r_old)
  do istep = 1,nmax
     call random_number(r_new)
     r_new(:) = r_old(:) + tau * (2.d0*r_new(:) - 1.d0)
     psi_new = psi(a,r_new)
     ratio = (psi_new / psi_old)**2
     call random_number(v)
     if (v < ratio) then
        r_old(:) = r_new(:)
        psi_old = psi_new
        n_accep = n_accep + 1.d0
     endif
     norm = norm + 1.d0
     energy = energy + e_loc(a,r_old)
  end do
  energy = energy / norm
  accep  = n_accep / norm
end subroutine metropolis_montecarlo

program qmc
  implicit none
  double precision, parameter :: a = 0.9d0
  double precision, parameter :: tau = 1.3d0
  integer*8       , parameter :: nmax = 100000
  integer         , parameter :: nruns = 30

  integer :: irun
  double precision :: X(nruns), Y(nruns)
  double precision :: ave, err

  do irun=1,nruns
     call metropolis_montecarlo(a,nmax,tau,X(irun),Y(irun))
  enddo
  call ave_error(X,nruns,ave,err)
  print *, 'E = ', ave, '+/-', err
  call ave_error(Y,nruns,ave,err)
  print *, 'A = ', ave, '+/-', err
end program qmc
    #+END_SRC

    #+begin_src sh :results output :exports both
gfortran hydrogen.f90 qmc_stats.f90 qmc_metropolis.f90 -o qmc_metropolis
./qmc_metropolis
    #+end_src
    #+RESULTS:
    :  E =  -0.49478505004797046      +/-   2.0493795299184956E-004
    :  A =   0.51737800000000000      +/-   4.1827406733181444E-004

** TODO Gaussian random number generator
   
   To obtain Gaussian-distributed random numbers, you can apply the
   [[https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform][Box Muller transform]] to uniform random numbers:

   \begin{eqnarray*}
   z_1 &=& \sqrt{-2 \ln u_1} \cos(2 \pi u_2) \\
   z_2 &=& \sqrt{-2 \ln u_1} \sin(2 \pi u_2) 
   \end{eqnarray*}

   Below is a Fortran implementation returning a Gaussian-distributed
   n-dimensional vector $\mathbf{z}$. This will be useful for the
   following sections.

 *Fortran*
   #+BEGIN_SRC f90 :tangle qmc_stats.f90
subroutine random_gauss(z,n)
  implicit none
  integer, intent(in) :: n
  double precision, intent(out) :: z(n)
  double precision :: u(n+1)
  double precision, parameter :: two_pi = 2.d0*dacos(-1.d0)
  integer :: i

  call random_number(u)
  if (iand(n,1) == 0) then
     ! n is even
     do i=1,n,2
        z(i)   = dsqrt(-2.d0*dlog(u(i))) 
        z(i+1) = z(i) * dsin( two_pi*u(i+1) )
        z(i)   = z(i) * dcos( two_pi*u(i+1) )
     end do
  else
     ! n is odd
     do i=1,n-1,2
        z(i)   = dsqrt(-2.d0*dlog(u(i))) 
        z(i+1) = z(i) * dsin( two_pi*u(i+1) )
        z(i)   = z(i) * dcos( two_pi*u(i+1) )
     end do
     z(n)   = dsqrt(-2.d0*dlog(u(n))) 
     z(n)   = z(n) * dcos( two_pi*u(n+1) )
  end if
end subroutine random_gauss
   #+END_SRC

** TODO Generalized Metropolis algorithm
   :PROPERTIES:
   :header-args:python: :tangle vmc_metropolis.py
   :header-args:f90: :tangle vmc_metropolis.f90
   :END:

   One can use more efficient numerical schemes to move the electrons.
   But in that case, the Metropolis accepation step has to be adapted
   accordingly: the acceptance
   probability $A$ is chosen so that it is consistent with the
   probability of leaving $\mathbf{r}_n$ and the probability of
   entering $\mathbf{r}_{n+1}$:

   \[ A(\mathbf{r}_{n} \rightarrow \mathbf{r}_{n+1}) = \min \left( 1,
   \frac{T(\mathbf{r}_{n+1} \rightarrow \mathbf{r}_{n}) P(\mathbf{r}_{n+1})}
   {T(\mathbf{r}_{n} \rightarrow \mathbf{r}_{n+1}) P(\mathbf{r}_{n})}
   \right)
   \]
   where $T(\mathbf{r}_n \rightarrow \mathbf{r}_{n+1})$ is the
   probability of transition from $\mathbf{r}_n$ to
   $\mathbf{r}_{n+1}$.

   In the previous example, we were using uniform random
   numbers. Hence, the transition probability was

   \[
   T(\mathbf{r}_{n} \rightarrow \mathbf{r}_{n+1}) & = &
   \text{constant}
   \]

   So the expression of $A$ was simplified to the ratios of the squared
   wave functions.
    
   Now, if instead of drawing uniform random numbers
   choose to draw Gaussian random numbers with mean 0 and variance
   $\tau$, the transition probability becomes:
    
   \[
   T(\mathbf{r}_{n} \rightarrow \mathbf{r}_{n+1}) & = &
   \frac{1}{(2\pi\,\tau)^{3/2}} \exp \left[ - \frac{\left(
   \mathbf{r}_{n+1} - \mathbf{r}_{n} \right)^2}{2\tau} \right]
   \]

    
   To sample even better the density, we can "push" the electrons
   into in the regions of high probability, and "pull" them away from
   the low-probability regions. This will mechanically increase the
   acceptance ratios and improve the sampling.

   To do this, we can add the drift vector

   \[
   \frac{\nabla [ \Psi^2 ]}{\Psi^2} = 2 \frac{\nabla \Psi}{\Psi} 
   \].
    
   The numerical scheme becomes a drifted diffusion:

   \[
   \mathbf{r}_{n+1} = \mathbf{r}_{n} + \tau \frac{\nabla
   \Psi(\mathbf{r})}{\Psi(\mathbf{r})} + \chi 
   \]

   where $\chi$ is a Gaussian random variable with zero mean and
   variance $\tau$.
   The transition probability becomes:
    
   \[
   T(\mathbf{r}_{n} \rightarrow \mathbf{r}_{n+1}) & = &
   \frac{1}{(2\pi\,\tau)^{3/2}} \exp \left[ - \frac{\left(
   \mathbf{r}_{n+1} - \mathbf{r}_{n} - \frac{\nabla
   \Psi(\mathbf{r}_n)}{\Psi(\mathbf{r}_n)} \right)^2}{2\,\tau} \right]
   \]
    

*** Exercise 1

     #+begin_exercise
     Write a function to compute the drift vector $\frac{\nabla \Psi(\mathbf{r})}{\Psi(\mathbf{r})}$.
     #+end_exercise
   
 *Python*
     #+BEGIN_SRC python :tangle hydrogen.py
def drift(a,r):
  ar_inv = -a/np.sqrt(np.dot(r,r))
  return r * ar_inv
     #+END_SRC

 *Fortran*
     #+BEGIN_SRC f90 :tangle hydrogen.f90
subroutine drift(a,r,b)
  implicit none
  double precision, intent(in)  :: a, r(3)
  double precision, intent(out) :: b(3)
  double precision :: ar_inv
  ar_inv = -a / dsqrt(r(1)*r(1) + r(2)*r(2) + r(3)*r(3))
  b(:) = r(:) * ar_inv
end subroutine drift
     #+END_SRC

*** Exercise 2

    #+begin_exercise
    Modify the previous program to introduce the drifted diffusion scheme.
    (This is a necessary step for the next section).
    #+end_exercise
   
 *Python*
    #+BEGIN_SRC python :results output
from hydrogen  import *
from qmc_stats import *

def MonteCarlo(a,tau,nmax):
    E = 0.
    N = 0.
    accep_rate = 0.
    sq_tau = np.sqrt(tau)
    r_old = np.random.normal(loc=0., scale=1.0, size=(3))
    d_old = drift(a,r_old)
    d2_old = np.dot(d_old,d_old)
    psi_old = psi(a,r_old)
    for istep in range(nmax):
        chi = np.random.normal(loc=0., scale=1.0, size=(3))
        r_new = r_old + tau * d_old + sq_tau * chi
        d_new = drift(a,r_new)
        d2_new = np.dot(d_new,d_new)
        psi_new = psi(a,r_new)
        # Metropolis
        prod = np.dot((d_new + d_old), (r_new - r_old))
        argexpo = 0.5 * (d2_new - d2_old)*tau + prod
        q = psi_new / psi_old
        q = np.exp(-argexpo) * q*q
        if np.random.uniform() < q:
            accep_rate += 1.
            r_old = r_new
            d_old = d_new
            d2_old = d2_new
            psi_old = psi_new
        N += 1.
        E += e_loc(a,r_old)
    return E/N, accep_rate/N


a = 0.9
nmax = 100000
tau = 1.0
X = [MonteCarlo(a,tau,nmax) for i in range(30)]
E, deltaE = ave_error([x[0] for x in X])
A, deltaA = ave_error([x[1] for x in X])
print(f"E = {E} +/- {deltaE}\nA = {A} +/- {deltaA}")
    #+END_SRC

    #+RESULTS:
    : E = -0.4949730317138491 +/- 0.00012478601801760644
    : A = 0.7887163333333334 +/- 0.00026834549840347617
   
 *Fortran*
    #+BEGIN_SRC f90
subroutine variational_montecarlo(a,tau,nmax,energy,accep_rate)
  implicit none
  double precision, intent(in)  :: a, tau
  integer*8       , intent(in)  :: nmax 
  double precision, intent(out) :: energy, accep_rate

  integer*8 :: istep
  double precision :: norm, sq_tau, chi(3), d2_old, prod, u
  double precision :: psi_old, psi_new, d2_new, argexpo, q
  double precision :: r_old(3), r_new(3)
  double precision :: d_old(3), d_new(3)
  double precision, external :: e_loc, psi

  sq_tau = dsqrt(tau)

  ! Initialization
  energy = 0.d0
  norm   = 0.d0
  accep_rate = 0.d0
  call random_gauss(r_old,3)
  call drift(a,r_old,d_old)
  d2_old = d_old(1)*d_old(1) + d_old(2)*d_old(2) + d_old(3)*d_old(3)
  psi_old = psi(a,r_old)

  do istep = 1,nmax
     call random_gauss(chi,3)
     r_new(:) = r_old(:) + tau * d_old(:) + chi(:)*sq_tau
     call drift(a,r_new,d_new)
     d2_new = d_new(1)*d_new(1) + d_new(2)*d_new(2) + d_new(3)*d_new(3)
     psi_new = psi(a,r_new)
     ! Metropolis
     prod = (d_new(1) + d_old(1))*(r_new(1) - r_old(1)) + &
          (d_new(2) + d_old(2))*(r_new(2) - r_old(2)) + &
          (d_new(3) + d_old(3))*(r_new(3) - r_old(3))
     argexpo = 0.5d0 * (d2_new - d2_old)*tau + prod
     q = psi_new / psi_old
     q = dexp(-argexpo) * q*q
     call random_number(u)
     if (u<q) then
        accep_rate = accep_rate + 1.d0
        r_old(:) = r_new(:)
        d_old(:) = d_new(:)
        d2_old = d2_new
        psi_old = psi_new
     end if
     norm = norm + 1.d0
     energy = energy + e_loc(a,r_old)
  end do
  energy = energy / norm
  accep_rate = accep_rate / norm
end subroutine variational_montecarlo

program qmc
  implicit none
  double precision, parameter :: a = 0.9
  double precision, parameter :: tau = 1.0
  integer*8       , parameter :: nmax = 100000
  integer         , parameter :: nruns = 30

  integer :: irun
  double precision :: X(nruns), accep(nruns)
  double precision :: ave, err

  do irun=1,nruns
     call variational_montecarlo(a,tau,nmax,X(irun),accep(irun))
  enddo
  call ave_error(X,nruns,ave,err)
  print *, 'E = ', ave, '+/-', err
  call ave_error(accep,nruns,ave,err)
  print *, 'A = ', ave, '+/-', err
end program qmc
    #+END_SRC

    #+begin_src sh :results output :exports both
gfortran hydrogen.f90 qmc_stats.f90 vmc_metropolis.f90 -o vmc_metropolis
./vmc_metropolis
    #+end_src

    #+RESULTS:
    :  E =  -0.49499990423528023      +/-   1.5958250761863871E-004
    :  A =   0.78861366666666655      +/-   3.5096729498002445E-004
     
* TODO Diffusion Monte Carlo
   :PROPERTIES:
   :header-args:python: :tangle dmc.py
   :header-args:f90: :tangle dmc.f90
   :END:
   
** TODO Hydrogen atom
   
*** Exercise 

     #+begin_exercise
     Modify the Metropolis VMC program to introduce the PDMC weight.
     In the limit $\tau \rightarrow 0$, you should recover the exact
     energy of H for any value of $a$.
     #+end_exercise
   
 *Python*
      #+BEGIN_SRC python :results output
from hydrogen  import *
from qmc_stats import *

def MonteCarlo(a,tau,nmax,Eref):
    E = 0.
    N = 0.
    accep_rate = 0.
    sq_tau = np.sqrt(tau)
    r_old = np.random.normal(loc=0., scale=1.0, size=(3))
    d_old = drift(a,r_old)
    d2_old = np.dot(d_old,d_old)
    psi_old = psi(a,r_old)
    w = 1.0
    for istep in range(nmax):
        chi = np.random.normal(loc=0., scale=1.0, size=(3))
        el = e_loc(a,r_old)
        w *= np.exp(-tau*(el - Eref))
        N += w
        E += w * el

        r_new = r_old + tau * d_old + sq_tau * chi
        d_new = drift(a,r_new)
        d2_new = np.dot(d_new,d_new)
        psi_new = psi(a,r_new)
        # Metropolis
        prod = np.dot((d_new + d_old), (r_new - r_old))
        argexpo = 0.5 * (d2_new - d2_old)*tau + prod
        q = psi_new / psi_old
        q = np.exp(-argexpo) * q*q
        # PDMC weight
        if np.random.uniform() < q:
            accep_rate += w
            r_old = r_new
            d_old = d_new
            d2_old = d2_new
            psi_old = psi_new
    return E/N, accep_rate/N


a = 0.9
nmax = 10000
tau = .1
X = [MonteCarlo(a,tau,nmax,-0.5) for i in range(30)]
E, deltaE = ave_error([x[0] for x in X])
A, deltaA = ave_error([x[1] for x in X])
print(f"E = {E} +/- {deltaE}\nA = {A} +/- {deltaA}")
      #+END_SRC

      #+RESULTS:
      : E = -0.49654807434947584 +/- 0.0006868522447409156
      : A = 0.9876193891840709 +/- 0.00041857361650995804
   
 *Fortran*
      #+BEGIN_SRC f90
subroutine variational_montecarlo(a,tau,nmax,energy,accep_rate)
  implicit none
  double precision, intent(in)  :: a, tau
  integer*8       , intent(in)  :: nmax 
  double precision, intent(out) :: energy, accep_rate

  integer*8 :: istep
  double precision :: norm, sq_tau, chi(3), d2_old, prod, u
  double precision :: psi_old, psi_new, d2_new, argexpo, q
  double precision :: r_old(3), r_new(3)
  double precision :: d_old(3), d_new(3)
  double precision, external :: e_loc, psi

  sq_tau = dsqrt(tau)
  
  ! Initialization
  energy = 0.d0
  norm   = 0.d0
  accep_rate = 0.d0
  call random_gauss(r_old,3)
  call drift(a,r_old,d_old)
  d2_old = d_old(1)*d_old(1) + d_old(2)*d_old(2) + d_old(3)*d_old(3)
  psi_old = psi(a,r_old)

  do istep = 1,nmax
     call random_gauss(chi,3)
     r_new(:) = r_old(:) + tau * d_old(:) + chi(:)*sq_tau
     call drift(a,r_new,d_new)
     d2_new = d_new(1)*d_new(1) + d_new(2)*d_new(2) + d_new(3)*d_new(3)
     psi_new = psi(a,r_new)
     ! Metropolis
     prod = (d_new(1) + d_old(1))*(r_new(1) - r_old(1)) + &
            (d_new(2) + d_old(2))*(r_new(2) - r_old(2)) + &
            (d_new(3) + d_old(3))*(r_new(3) - r_old(3))
     argexpo = 0.5d0 * (d2_new - d2_old)*tau + prod
     q = psi_new / psi_old
     q = dexp(-argexpo) * q*q
     call random_number(u)
     if (u<q) then
        accep_rate = accep_rate + 1.d0
        r_old(:) = r_new(:)
        d_old(:) = d_new(:)
        d2_old = d2_new
        psi_old = psi_new
     end if
     norm = norm + 1.d0
     energy = energy + e_loc(a,r_old)
  end do
  energy = energy / norm
  accep_rate = accep_rate / norm
end subroutine variational_montecarlo

program qmc
  implicit none
  double precision, parameter :: a = 0.9
  double precision, parameter :: tau = 1.0
  integer*8       , parameter :: nmax = 100000
  integer         , parameter :: nruns = 30

  integer :: irun
  double precision :: X(nruns), accep(nruns)
  double precision :: ave, err

  do irun=1,nruns
     call variational_montecarlo(a,tau,nmax,X(irun),accep(irun))
  enddo
  call ave_error(X,nruns,ave,err)
  print *, 'E = ', ave, '+/-', err
  call ave_error(accep,nruns,ave,err)
  print *, 'A = ', ave, '+/-', err
end program qmc
      #+END_SRC

      #+begin_src sh :results output :exports both
gfortran hydrogen.f90 qmc_stats.f90 vmc_metropolis.f90 -o vmc_metropolis
./vmc_metropolis
      #+end_src

      #+RESULTS:
      :  E =  -0.49499990423528023      +/-   1.5958250761863871E-004
      :  A =   0.78861366666666655      +/-   3.5096729498002445E-004
     

** TODO Dihydrogen

   We will now consider the H_2 molecule in a minimal basis composed of the
   $1s$ orbitals of the hydrogen atoms:

   $$
   \Psi(\mathbf{r}_1, \mathbf{r}_2) =
   \exp(-(\mathbf{r}_1 - \mathbf{R}_A)) + 
   $$
   where $\mathbf{r}_1$ and $\mathbf{r}_2$ denote the electron
   coordinates and $\mathbf{R}_A$ and $\mathbf{R}_B$ the coordinates of
   the nuclei.

   
* Appendix                                                         :noexport:

** Gaussian sampling                                               :noexport:
   :PROPERTIES:
   :header-args:python: :tangle qmc_gaussian.py
   :header-args:f90: :tangle qmc_gaussian.f90
   :END:

   We will now improve the sampling and allow to sample in the whole
   3D space, correcting the bias related to the sampling in the box.

   Instead of drawing uniform random numbers, we will draw Gaussian
   random numbers centered on 0 and with a variance of 1.
   
   Now the sampling probability can be inserted into the equation of the energy:
   
   \[
   E = \frac{\int P(\mathbf{r}) \frac{\left[\Psi(\mathbf{r})\right]^2}{P(\mathbf{r})}\, \frac{\hat{H} \Psi(\mathbf{r})}{\Psi(\mathbf{r})}\,d\mathbf{r}}{\int P(\mathbf{r}) \frac{\left[\Psi(\mathbf{r}) \right]^2}{P(\mathbf{r})} d\mathbf{r}}
   \]

   with the Gaussian probability

   \[
   P(\mathbf{r}) = \frac{1}{(2 \pi)^{3/2}}\exp\left( -\frac{\mathbf{r}^2}{2} \right).
   \]

   As the coordinates are drawn with probability $P(\mathbf{r})$, the
   average energy can be computed as

   $$
   E \approx \frac{\sum_i w_i E_L(\mathbf{r}_i)}{\sum_i w_i}, \;\;
   w_i = \frac{\left[\Psi(\mathbf{r}_i)\right]^2}{P(\mathbf{r}_i)} \delta \mathbf{r}
   $$

   
*** Exercise

    #+begin_exercise
    Modify the program of the previous section to sample with
    Gaussian-distributed random numbers. Can you see an reduction in
    the statistical error?
    #+end_exercise

 *Python*
    #+BEGIN_SRC python :results output
from hydrogen  import *
from qmc_stats import *

norm_gauss = 1./(2.*np.pi)**(1.5)
def gaussian(r):
    return norm_gauss * np.exp(-np.dot(r,r)*0.5)

def MonteCarlo(a,nmax):
    E = 0.
    N = 0.
    for istep in range(nmax):
        r = np.random.normal(loc=0., scale=1.0, size=(3))
        w = psi(a,r)
        w = w*w / gaussian(r)
        N += w
        E += w * e_loc(a,r)
    return E/N

a = 0.9
nmax = 100000
X = [MonteCarlo(a,nmax) for i in range(30)]
E, deltaE = ave_error(X)
print(f"E = {E} +/- {deltaE}")
    #+END_SRC

    #+RESULTS:
    : E = -0.49511014287471955 +/- 0.00012402022172236656
   
 *Fortran*
    #+BEGIN_SRC f90
double precision function gaussian(r)
  implicit none
  double precision, intent(in) :: r(3)
  double precision, parameter :: norm_gauss = 1.d0/(2.d0*dacos(-1.d0))**(1.5d0)
  gaussian = norm_gauss * dexp( -0.5d0 * (r(1)*r(1) + r(2)*r(2) + r(3)*r(3) ))
end function gaussian


subroutine gaussian_montecarlo(a,nmax,energy)
  implicit none
  double precision, intent(in)  :: a
  integer*8       , intent(in)  :: nmax 
  double precision, intent(out) :: energy

  integer*8 :: istep

  double precision :: norm, r(3), w

  double precision, external :: e_loc, psi, gaussian

  energy = 0.d0
  norm   = 0.d0
  do istep = 1,nmax
     call random_gauss(r,3)
     w = psi(a,r) 
     w = w*w / gaussian(r)
     norm = norm + w
     energy = energy + w * e_loc(a,r)
  end do
  energy = energy / norm
end subroutine gaussian_montecarlo

program qmc
  implicit none
  double precision, parameter :: a = 0.9
  integer*8       , parameter :: nmax = 100000
  integer         , parameter :: nruns = 30

  integer :: irun
  double precision :: X(nruns)
  double precision :: ave, err

  do irun=1,nruns
     call gaussian_montecarlo(a,nmax,X(irun))
  enddo
  call ave_error(X,nruns,ave,err)
  print *, 'E = ', ave, '+/-', err
end program qmc
    #+END_SRC

    #+begin_src sh :results output :exports both
gfortran hydrogen.f90 qmc_stats.f90 qmc_gaussian.f90 -o qmc_gaussian
./qmc_gaussian
    #+end_src

    #+RESULTS:
    :  E =  -0.49517104619091717      +/-   1.0685523607878961E-004

** Improved sampling with $\Psi^2$                                 :noexport:

*** Importance sampling
   :PROPERTIES:
   :header-args:python: :tangle vmc.py
   :header-args:f90: :tangle vmc.f90
   :END:

    To generate the probability density $\Psi^2$, we consider a
    diffusion process characterized by a time-dependent density
    $[\Psi(\mathbf{r},t)]^2$, which obeys the Fokker-Planck equation:

    \[
    \frac{\partial \Psi^2}{\partial t} = \sum_i D
    \frac{\partial}{\partial \mathbf{r}_i} \left(
    \frac{\partial}{\partial \mathbf{r}_i} - F_i(\mathbf{r}) \right)
    [\Psi(\mathbf{r},t)]^2.
    \]
   
    $D$ is the diffusion constant and $F_i$ is the i-th component of a
    drift velocity caused by an external potential. For a stationary
    density, \( \frac{\partial \Psi^2}{\partial t} = 0 \), so

    \begin{eqnarray*}
    0 & = & \sum_i D
    \frac{\partial}{\partial \mathbf{r}_i} \left(
    \frac{\partial}{\partial \mathbf{r}_i} - F_i(\mathbf{r}) \right)
    [\Psi(\mathbf{r})]^2 \\
    0 & = & \sum_i D
    \frac{\partial}{\partial \mathbf{r}_i} \left(
    \frac{\partial [\Psi(\mathbf{r})]^2}{\partial \mathbf{r}_i} -
    F_i(\mathbf{r})\,[\Psi(\mathbf{r})]^2 \right) \\
    0 & = &
    \frac{\partial^2 \Psi^2}{\partial \mathbf{r}_i^2} -
    \frac{\partial   F_i   }{\partial \mathbf{r}_i}[\Psi(\mathbf{r})]^2  - 
    \frac{\partial   \Psi^2}{\partial \mathbf{r}_i} F_i(\mathbf{r})
    \end{eqnarray*}

    we search for a drift function which satisfies 

    \[
    \frac{\partial^2 \Psi^2}{\partial \mathbf{r}_i^2} =
    [\Psi(\mathbf{r})]^2 \frac{\partial   F_i   }{\partial \mathbf{r}_i} + 
    \frac{\partial   \Psi^2}{\partial \mathbf{r}_i} F_i(\mathbf{r})
    \]

    to obtain a second derivative on the left, we need the drift to be
    of the form
    \[
    F_i(\mathbf{r}) = g(\mathbf{r}) \frac{\partial \Psi^2}{\partial \mathbf{r}_i}
    \]

    \[
    \frac{\partial^2 \Psi^2}{\partial \mathbf{r}_i^2} =
    [\Psi(\mathbf{r})]^2 \frac{\partial
    g(\mathbf{r})}{\partial \mathbf{r}_i}\frac{\partial \Psi^2}{\partial \mathbf{r}_i} + 
    [\Psi(\mathbf{r})]^2 g(\mathbf{r}) \frac{\partial^2
    \Psi^2}{\partial \mathbf{r}_i^2} + 
    \frac{\partial   \Psi^2}{\partial \mathbf{r}_i} 
    g(\mathbf{r}) \frac{\partial \Psi^2}{\partial \mathbf{r}_i}
    \]
   
    $g = 1 / \Psi^2$ satisfies this equation, so 

    \[
    F(\mathbf{r}) = \frac{\nabla [\Psi(\mathbf{r})]^2}{[\Psi(\mathbf{r})]^2} = 2 \frac{\nabla
    \Psi(\mathbf{r})}{\Psi(\mathbf{r})} = 2 \nabla \left( \log \Psi(\mathbf{r}) \right)
    \]

    In statistical mechanics, Fokker-Planck trajectories are generated
    by a Langevin equation:

    \[
     \frac{\partial \mathbf{r}(t)}{\partial t} = 2D \frac{\nabla
     \Psi(\mathbf{r}(t))}{\Psi} + \eta
    \]

    where $\eta$ is a normally-distributed fluctuating random force.

    Discretizing this differential equation gives the following drifted
    diffusion scheme:

    \[
    \mathbf{r}_{n+1} = \mathbf{r}_{n} + \tau\, 2D \frac{\nabla
    \Psi(\mathbf{r})}{\Psi(\mathbf{r})} + \chi 
    \]
    where $\chi$ is a Gaussian random variable with zero mean and
    variance $\tau\,2D$.
   
**** Exercise 2

     #+begin_exercise
     Sample $\Psi^2$ approximately using the drifted diffusion scheme,
     with a diffusion constant $D=1/2$. You can use a time step of
     0.001 a.u.
     #+end_exercise
   
 *Python*
      #+BEGIN_SRC python :results output
from hydrogen  import *
from qmc_stats import *

def MonteCarlo(a,tau,nmax):
    sq_tau = np.sqrt(tau)

    # Initialization
    E = 0.
    N = 0.
    r_old = np.random.normal(loc=0., scale=1.0, size=(3))

    for istep in range(nmax):
        d_old = drift(a,r_old)
        chi = np.random.normal(loc=0., scale=1.0, size=(3))
        r_new = r_old + tau * d_old + chi*sq_tau
        N += 1.
        E += e_loc(a,r_new)
        r_old = r_new
    return E/N


a = 0.9
nmax = 100000
tau = 0.2
X = [MonteCarlo(a,tau,nmax) for i in range(30)]
E, deltaE = ave_error(X)
print(f"E = {E} +/- {deltaE}")
      #+END_SRC

      #+RESULTS:
      : E = -0.4858534479298907 +/- 0.00010203236131158794

 *Fortran*
      #+BEGIN_SRC f90
subroutine variational_montecarlo(a,tau,nmax,energy)
  implicit none
  double precision, intent(in)  :: a, tau
  integer*8       , intent(in)  :: nmax 
  double precision, intent(out) :: energy

  integer*8 :: istep
  double precision :: norm, r_old(3), r_new(3), d_old(3), sq_tau, chi(3)
  double precision, external :: e_loc

  sq_tau = dsqrt(tau)
  
  ! Initialization
  energy = 0.d0
  norm   = 0.d0
  call random_gauss(r_old,3)

  do istep = 1,nmax
     call drift(a,r_old,d_old)
     call random_gauss(chi,3)
     r_new(:) = r_old(:) + tau * d_old(:) + chi(:)*sq_tau
     norm = norm + 1.d0
     energy = energy + e_loc(a,r_new)
     r_old(:) = r_new(:)
  end do
  energy = energy / norm
end subroutine variational_montecarlo

program qmc
  implicit none
  double precision, parameter :: a = 0.9
  double precision, parameter :: tau = 0.2
  integer*8       , parameter :: nmax = 100000
  integer         , parameter :: nruns = 30

  integer :: irun
  double precision :: X(nruns)
  double precision :: ave, err

  do irun=1,nruns
     call variational_montecarlo(a,tau,nmax,X(irun))
  enddo
  call ave_error(X,nruns,ave,err)
  print *, 'E = ', ave, '+/-', err
end program qmc
      #+END_SRC

      #+begin_src sh :results output :exports both
gfortran hydrogen.f90 qmc_stats.f90 vmc.f90 -o vmc
./vmc
      #+end_src

      #+RESULTS:
      :  E =  -0.48584030499187431      +/-   1.0411743995438257E-004