Python中qutip的用法有哪些

from qutip import *
import numpy as np
import matplotlib.pyplot as plt

n=2#原子數(shù)
j = n//2
psi0 = spin_coherent(j, np.pi/3, 0)#設置系統(tǒng)的初態(tài)為自旋相干態(tài)

Jp=destroy(2*j+1).dag()#升算符
J_=destroy(2*j+1)#降算符
Jz=(Jp*J_-J_*Jp)/2#Jz

H=Jz**2#系統(tǒng)的哈密頓量

tlist=np.linspace(0,3,100)#時間列表
result=mesolve(H,psi0,tlist)#態(tài)隨時間的演化

theta=np.linspace(0, np.pi, 50)
phi=np.linspace(0, 2*np.pi, 50)

#分別計算四個狀態(tài)下的 husimi q函數(shù)
Q1, THETA1, PHI1 = spin_q_function(result.states[0], theta, phi)
Q2, THETA2, PHI2 = spin_q_function(result.states[30], theta, phi)
Q3, THETA3, PHI3 = spin_q_function(result.states[60], theta, phi)
Q4, THETA4, PHI4 = spin_q_function(result.states[90], theta, phi)

#在四個子圖中分別畫出四個狀態(tài)下的husimi q函數(shù)
fig = plt.figure(dpi=150,constrained_layout=1)
ax1 = fig.add_subplot(221,projection='3d')
ax2 = fig.add_subplot(222,projection='3d')
ax3 = fig.add_subplot(223,projection='3d')
ax4 = fig.add_subplot(224,projection='3d')

plot_spin_distribution_3d(Q1, THETA1, PHI1,fig=fig,ax=ax1)
plot_spin_distribution_3d(Q2, THETA2, PHI2,fig=fig,ax=ax2)
plot_spin_distribution_3d(Q3, THETA3, PHI3,fig=fig,ax=ax3)
plot_spin_distribution_3d(Q4, THETA4, PHI4,fig=fig,ax=ax4)

for ax in [ax1,ax2,ax3,ax4]:
 ax.view_init(0.5*np.pi, 0)
 ax.axis('off')#不顯示坐標軸

fig.show()

from qutip import *
import numpy as np
import matplotlib.pyplot as plt

alpha=1#相干光的參數(shù)alpha
n=2#原子數(shù)
j = n/2

psi0 = tensor(coherent(10,alpha),spin_coherent(j, 0, 0))#設置系統(tǒng)的初態(tài)

a=destroy(10)#光場的湮滅算符
a_plus=a.dag()#光場的產(chǎn)生算符
Jp=destroy(n+1).dag()#原子的升算符
J_=destroy(n+1)#原子的降算符
Jx=(Jp+J_)/2#原子的Jx算符
Jy=(Jp-J_)/(2j)#原子的Jy算符,這里的j是虛數(shù)單位
Jz=(Jp*J_-J_*Jp)/2#原子的Jz算符

H=tensor(a,Jp)+tensor(a_plus,J_)#系統(tǒng)的哈密頓量
tlist=np.linspace(0,10,1000)#時間列表
result=mesolve(H,psi0,tlist)#態(tài)隨時間的演化

fig=plt.figure()
ax1 = fig.add_subplot(221)
ax2 = fig.add_subplot(222)
ax3 = fig.add_subplot(223)
ax4 = fig.add_subplot(224)

ax1.plot(tlist,expect(tensor(qeye(10),Jx),result.states))#Jx的平均值隨時間變化圖
ax2.plot(tlist,expect(tensor(qeye(10),Jy),result.states))#Jy的平均值隨時間變化圖
ax3.plot(tlist,expect(tensor(qeye(10),Jz),result.states))#Jz的平均值隨時間變化圖
ax4.plot(tlist,expect(tensor(qeye(10),Jx**2+Jy**2+Jz*2),result.states))#J平方的平均值隨時間變化圖

fig.subplots_adjust(top=None,bottom=None,left=None,right=None,wspace=0.4,hspace=0.4)#設置子圖間距
fig.show()