# Flux qubit wavefunctions¶

Robert Johansson ([email protected])

In [1]:
%pylab inline

Populating the interactive namespace from numpy and matplotlib

In [2]:
from scipy import *
import sys

In [3]:
from wavefunction import *


### Problem parameters¶

In [4]:
#
# Define how many states should be calculated
# truncate infinite series to [-N,N]
N = 5;
nstates = 2*N+1

##
# Costants
#
h = 6.626e-34
h_ = h/(2*pi)
e = 1.602e-19
phi0 = h/(2*e)

Ej = 1.0   # unit
#Cj = 1
#Ec = e**2/(2*Cj)
alpha = 0.8

#Ej = 40 * Ec
Ec = Ej/40.0
Cj = e**2/(2*Ec)

Mp = 2*Cj*(phi0/(2*pi))**2
Mm = Mp * (1+2*alpha)

fi = 0.45
ff = 0.55
fs = 0.001

#
# Range of flux
#
F = arange(fi, ff, fs) # 0.49:0.0001:0.51;
idx = 0

In [5]:
##
# Set up vectors and matrices
#
k = arange(-N,N+1) #-N:N;
l = arange(-N,N+1) #-N:N;
bp = h_**2 * k**2 / (2*Mp);
bm = h_**2 * l**2 / (2*Mm);
Bp = diag(bp);
Bm = diag(bm);
I = eye(nstates);

# Construct M
M0 = zeros((nstates**2,nstates**2), dtype=complex)
# B^{p}C term
M0 = M0 + recastMtoT(nstates, Bp, I);
# CB^{m} term
M0 = M0 + recastMtoT(nstates, I, Bm);
# E_{J}(2-alpha)C term
M0 = M0 + Ej*(2+alpha)*recastMtoT(nstates, I, I);
# Ej/2 * D^{(-1,0)}CD^{(0,-1)} term
M0 = M0 - Ej/2*recastMtoT(nstates, kd(nstates,-1,0), kd(nstates,0,-1));
# Ej/2 * D^{(-1,0)}CD^{(0,+1)} term
M0 = M0 - Ej/2*recastMtoT(nstates, kd(nstates,-1,0), kd(nstates,0,+1));
# Ej/2 * D^{(+1,0)}CD^{(0,-1)} term
M0 = M0 - Ej/2*recastMtoT(nstates, kd(nstates,+1,0), kd(nstates,0,-1));
# Ej/2 * D^{(+1,0)}CD^{(0,+1)} term
M0 = M0 - Ej/2*recastMtoT(nstates, kd(nstates,+1,0), kd(nstates,0,+1));

E = zeros((len(F), nstates**2)).astype(float)
t01 = zeros(len(F)).astype(float)
t02 = zeros(len(F)).astype(float)
t03 = zeros(len(F)).astype(float)
t12 = zeros(len(F)).astype(float)
t13 = zeros(len(F)).astype(float)
t23 = zeros(len(F)).astype(float)

kdp1 = kdp(nstates, 2)+kdp(nstates, -2)
kdp2 = kdp(nstates, 2)-kdp(nstates, -2)

M1 = recastMtoT(nstates, I, kd(nstates,0,-2))
M2 = recastMtoT(nstates, I, kd(nstates,0,+2))

for f in F:

# alpha * Ej/2 * cos(2*pi*f)* C D^{(0,-2)} term
M = M0 - (alpha*Ej/2*cos(2*pi*f) + sqrt(-1) * alpha*Ej/2*sin(2*pi*f))* M1;
# alpha * Ej/2 * cos(2*pi*f)* C D^{(0,+2)} term
M = M - (alpha*Ej/2*cos(2*pi*f) - sqrt(-1) * alpha*Ej/2*sin(2*pi*f))* M2;
# alpha * Ej/2 * cos(2*pi*f)* C D^{(0,-2)} term
##M = M - sqrt(-1) * alpha*Ej/2*sin(2*pi*f) * recastMtoT(nstates, I, kd(nstates,0,-2));
# alpha * Ej/2 * cos(2*pi*f)* C D^{(0,+2)} term
##M = M + sqrt(-1) * alpha*Ej/2*sin(2*pi*f) * recastMtoT(nstates, I, kd(nstates,0,+2));

# Solve EVP: Mx=Ex
eval, evec = eigenvectors_sorted(M)
#eval, evec = solve_eigenproblem(M)

#S(:,:,idx) = V;
E[idx][:] = transpose(eval)

# Calc transition elements for driving operator O
phia0 = 0.15*phi0;
O = -2*pi*alpha*Ej * phia0/phi0 * (sin(2*pi*f)/2 * kdp1 + cos(2*pi*f)/(2*1j) * kdp2);

c0 = evec[0,:]
c1 = evec[3,:]
c2 = evec[4,:]
c3 = evec[7,:]

t01[idx] = abs(dot(conj(c0), dot(O, c1)))
t02[idx] = abs(dot(conj(c0), dot(O, c2)))
t03[idx] = abs(dot(conj(c0), dot(O, c3)))
t12[idx] = abs(dot(conj(c1), dot(O, c2)))
t13[idx] = abs(dot(conj(c1), dot(O, c3)))
t23[idx] = abs(dot(conj(c2), dot(O, c3)))

idx = idx + 1;


### Plot eigenvalues¶

In [6]:
fig, ax = subplots(1, 1, figsize=(12,10))

for idx in [0, 3, 4, 7, 9, 10]:
ax.plot(F, E[:,idx])


### Plot eigenvalues¶

In [7]:
fig, ax = subplots(1, 1, figsize=(12,10))

for idx in [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]:
ax.plot(F, E[:,idx])


### Plot transition rates¶

In [8]:
fig, ax = subplots(1, 1, figsize=(12,10))

ax.plot(F, t01, label='t01')
ax.plot(F, t02, label='t02')
ax.plot(F, t03, label='t03')
ax.plot(F, t12, label='t12')
ax.plot(F, t13, label='t13')
ax.plot(F, t23, label='t23')

ax.legend();

In [8]: