文档中心
配置文件示例¶
上一节结尾我们给出了常见的几种配置文件示例，在这一节中，我们将给出更多配置文件示例、
在设置量子线路时，使用pauli算符进行自定义拟设。
general = {
task = energy
backend = CPU_SINGLE_THREAD
license = XXXXX
}
mole = {
geoms = {
H 0.000000 0.000000 0.356115
H 0.000000 0.000000 -0.356115
}
charge = 0
spin = 1
basis = sto-3g
}
ansatz = User-define{
pauli = {
X0 Y2 : -0.125000
X0 Y2 Z3 : -0.125000
X0 Z1 Y2 : -0.125000
X0 Z1 Y2 Z3 : -0.125000
Y0 X2 : 0.125000
Y0 X2 Z3 : 0.125000
Y0 Z1 X2 : 0.125000
Y0 Z1 X2 Z3 : 0.125000
}
mapping = BK
}
optimizer = NELDER-MEAD {
learning_rate = 0.1
init_para_type = MP2
slices = 1
iters = 1000
fcalls = 1000
xatol = 1e-6
fatol = 1e-6
}
使用参数线性组合对自定义线路中的泡利项进行参数优化的限制。
general = {
task = energy
backend = CPU_SINGLE_THREAD
license = XXXXX
}
mole = {
geoms = {
H 0 0 0.38
Li 0 0 -1.13
}
charge = 0
spin = 1
basis = sto-3g
active = 4,4
}
ansatz = User-define{
pauli = {
X0 Y2 : -0.125
X0 Y2 Z3 : -0.125
X0 Z1 Y2 : -0.125
X0 Z1 Y2 Z3 : -0.125
Y0 X2 : 0.125
Y0 X2 Z3 : 0.125
Y0 Z1 X2 : 0.125
Y0 Z1 X2 Z3 : 0.125
}
mapping = BK
}
optimizer = NELDER_MEAD {
learning_rate = 0.1
init_para_type = Zero
# There are 8 parameters x1,x2,...,x8 since there are 8 pauli items
# only two variables t1,t2 is used for optimizing
# x1,x2,x3,x4 = t2
# x5,x6,x7,x8 = t1
parameter_matrix = {
0 0 0 0 1 1 1 1
1 1 1 1 0 0 0 0
}
slices = 1
iters = 200
fcalls = 200
xatol = 1e-4
fatol = 1e-4
}
在设置量子线路时，使用originIR进行自定义拟设。
general = {
task = energy
backend = CPU_SINGLE_THREAD
license = XXXXX
}
mole = {
geoms = {
H 0.000000 0.000000 0.356115
H 0.000000 0.000000 -0.356115
}
charge = 0
spin = 1
basis = sto-3g
}
ansatz = User-define{
circuit = {
H q[0]
RX q[2],(1.5707963)
CNOT q[0],q[3]
CNOT q[1],q[3]
CNOT q[2],q[3]
RZ q[3],(1.5707963)
CNOT q[0],q[3]
CNOT q[1],q[3]
CNOT q[2],q[3]
DAGGER
H q[0]
RX q[2],(1.5707963)
ENDDAGGER
}
mapping = BK
}
optimizer = NELDER-MEAD {
learning_rate = 0.1
init_para_type = MP2
slices = 1
iters = 1000
fcalls = 1000
xatol = 1e-6
fatol = 1e-6
}
定义分子构型时，直接使用自定义哈密顿量来指定所需要计算的分子。
general = {
task = energy
backend = CPU_SINGLE_THREAD
license = XXXXX
}
mole = {
hamiltonian = {
: -0.059663
X0 Z1 X2 : 0.044918
X0 Z1 X2 Z3 : 0.044918
Y0 Z1 Y2 : 0.044918
Y0 Z1 Y2 Z3 : 0.044918
Z0 : 0.175755
Z0 Z1 : 0.175755
Z0 Z1 Z2 : 0.167143
Z0 Z1 Z2 Z3 : 0.167143
Z0 Z2 : 0.122225
Z0 Z2 Z3 : 0.122225
Z1 : 0.170014
Z1 Z2 Z3 : -0.236656
Z1 Z3 : 0.175702
Z2 : -0.236656
}
# nelec is needed when the hamiltonian is user-defined
nelec = 2
}
ansatz = UCC {
excited_level = SD
mapping = BK
}
optimizer = NELDER_MEAD {
learning_rate = 0.1
init_para_type = Zero
slices = 1
iters = 200
fcalls = 200
xatol = 1e-4
fatol = 1e-4
}
执行势能面曲线扫描时，扫描三个原子间的键角，定义为PES_values中四个不同的值。
general = {
task = PES
backend = CPU_SINGLE_THREAD
license = XXXXX
PES_atoms = 1,2,3
PES_values = 30,60,90,120
}
mole = {
geoms = {
H 0.625276 0.625276 0.625276
C 0.000000 0.000000 0.000000
H -0.625276 -0.625276 0.625276
H -0.625276 0.625276 -0.625276
H 0.625276 -0.625276 -0.625276
}
charge = 0
spin = 1
basis = sto-3g
active = 4,4
}
ansatz = UCC{
excited_level = SD
mapping = BK
}
optimizer = SLSQP {
slices = 1
learning_rate = 0.1
init_para_type = MP2
iters = 200
fcalls = 200
xatol = 1e-4
fatol = 1e-4
}
这个例子中，我们使用GAQPSO优化器进行变分量子线路的优化。在这个优化算法中，我们需要额外设置一些参数算法相关的参数。pso_wi指的是最大惯性权重，pso_we指的是最小惯性权重，pso_c1设置粒子向自身最好位置方向的加速常数，pso_c2设置粒子向全局最好位置方向的加速常数；pso_glr是指使用GD或SPSA更新粒子位置时的学习率，pso_deltap设置使用GD时的微扰变量；pso_thres/pso_thresf是指目标函数在当前与上一步迭代的差值阈值，pso_nearenough指粒子距离全局最优点G的距离阈值，pso_cmax设置使用局部搜索的最大次数，pso_alpha/pso_alphae 扩张-收缩系数；下面是一些算法的全局参数设置，pso_repeatn设置算法最大迭代次数，pso_seed设置随机数种子，方便复现结果，pso_n设置种群数量，pso_prefix设置输出结果默认存放的文件夹名称。
general = {
task = energy
backend = CPU_SINGLE_THREAD
license = XXXXX
}
mole = {
geoms = {
H 0.000000 0.000000 0.356115
H 0.000000 0.000000 -0.356115
}
charge = 0
spin = 1
basis = sto-3g
}
ansatz = UCC {
excited_level = SD
mapping = BK
}
optimizer = GAQPSO {
learning_rate = 0.01
init_para_type = MP2
pso_wi = 0.8
pso_we = 0.8
pso_c1 = 2.0
pso_c2 = 3.0
pso_glr = 0.01
pso_deltap = 1e-4
pso_thres = 0.001
pso_thresf = 1e-5
pso_nearenough = 1e-2
pso_alpha = 1.0
pso_alphae = 0.5
pso_repeatn = 1
pso_seed = 300
pso_n = 20
pso_cmax = 2
pso_prefix = pso_result
hamiltonian_simulation_slices = 1
iters = 100
xatol = 1e-8
fatol = 1e-8
}