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update examples and docs #62

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126 changes: 63 additions & 63 deletions examples/CMopt_NV.jl
100644 → 100755
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using QuanEstimation
using Random
using LinearAlgebra

# initial state
rho0 = zeros(ComplexF64, 6, 6)
rho0[1:4:5, 1:4:5] .= 0.5
# Hamiltonian
sx = [0. 1.; 1. 0.]
sy = [0. -im; im 0.]
sz = [1. 0.; 0. -1.]
s1 = [0. 1. 0.; 1. 0. 1.; 0. 1. 0.]/sqrt(2)
s2 = [0. -im 0.; im 0. -im; 0. im 0.]/sqrt(2)
s3 = [1. 0. 0.; 0. 0. 0.; 0. 0. -1.]
Is = I1, I2, I3 = [kron(I(3), sx), kron(I(3), sy), kron(I(3), sz)]
S = S1, S2, S3 = [kron(s1, I(2)), kron(s2, I(2)), kron(s3, I(2))]
B = B1, B2, B3 = [5.0e-4, 5.0e-4, 5.0e-4]
# All numbers are divided by 100 in this example
# for better calculation accurancy
cons = 100
D = (2pi*2.87*1000)/cons
gS = (2pi*28.03*1000)/cons
gI = (2pi*4.32)/cons
A1 = (2pi*3.65)/cons
A2 = (2pi*3.03)/cons
H0 = sum([D*kron(s3^2, I(2)), sum(gS*B.*S), sum(gI*B.*Is),
A1*(kron(s1, sx) + kron(s2, sy)), A2*kron(s3, sz)])
# derivatives of the free Hamiltonian on B1, B2 and B3
dH = gS*S+gI*Is
# control Hamiltonians
Hc = [S1, S2, S3]
# dissipation
decay = [[S3, 2pi/cons]]
# generation of a set of POVM basis
dim = size(rho0, 1)
POVM_basis = [QuanEstimation.basis(dim, i)*QuanEstimation.basis(dim, i)'
for i in 1:dim]
# time length for the evolution
tspan = range(0., 2., length=4000)
# control and measurement optimization
opt = QuanEstimation.CMopt(ctrl_bound=[-0.2,0.2], seed=1234)

##==========choose comprehensive optimization algorithm==========##
##-------------algorithm: DE---------------------##
alg = QuanEstimation.DE(p_num=10, max_episode=1000, c=1.0, cr=0.5)
# input the dynamics data
dynamics = QuanEstimation.Lindblad(opt, tspan, rho0, H0, dH, Hc,
decay=decay)
# objective function: CFI
obj = QuanEstimation.CFIM_obj()
# run the comprehensive optimization problem
QuanEstimation.run(opt, alg, obj, dynamics; savefile=false)

##-------------algorithm: PSO---------------------##
# alg = QuanEstimation.PSO(p_num=10, max_episode=[1000,100], c0=1.0,
# c1=2.0, c2=2.0)
# # input the dynamics data
# dynamics = QuanEstimation.Lindblad(opt, tspan, rho0, H0, dH, Hc,
# decay=decay)
# # objective function: CFI
# obj = QuanEstimation.CFIM_obj()
# # run the comprehensive optimization problem
# QuanEstimation.run(opt, alg, obj, dynamics; savefile=false)
using QuanEstimation
using Random
using LinearAlgebra
# initial state
rho0 = zeros(ComplexF64, 6, 6)
rho0[1:4:5, 1:4:5] .= 0.5
# Hamiltonian
sx = [0. 1.; 1. 0.]
sy = [0. -im; im 0.]
sz = [1. 0.; 0. -1.]
s1 = [0. 1. 0.; 1. 0. 1.; 0. 1. 0.]/sqrt(2)
s2 = [0. -im 0.; im 0. -im; 0. im 0.]/sqrt(2)
s3 = [1. 0. 0.; 0. 0. 0.; 0. 0. -1.]
Is = I1, I2, I3 = [kron(I(3), sx), kron(I(3), sy), kron(I(3), sz)]
S = S1, S2, S3 = [kron(s1, I(2)), kron(s2, I(2)), kron(s3, I(2))]
B = B1, B2, B3 = [5.0e-4, 5.0e-4, 5.0e-4]
# All numbers are divided by 100 in this example
# for better calculation accurancy
cons = 100
D = (2pi*2.87*1000)/cons
gS = (2pi*28.03*1000)/cons
gI = (2pi*4.32)/cons
A1 = (2pi*3.65)/cons
A2 = (2pi*3.03)/cons
H0 = sum([D*kron(s3^2, I(2)), sum(gS*B.*S), sum(gI*B.*Is),
A1*(kron(s1, sx) + kron(s2, sy)), A2*kron(s3, sz)])
# derivatives of the free Hamiltonian on B1, B2 and B3
dH = gS*S+gI*Is
# control Hamiltonians
Hc = [S1, S2, S3]
# dissipation
decay = [[S3, 2pi/cons]]
# generation of a set of POVM basis
dim = size(rho0, 1)
POVM_basis = [QuanEstimation.basis(dim, i)*QuanEstimation.basis(dim, i)'
for i in 1:dim]
# time length for the evolution
tspan = range(0., 2., length=4000)
# control and measurement optimization
opt = QuanEstimation.CMopt(ctrl_bound=[-0.2,0.2], seed=1234)
##==========choose comprehensive optimization algorithm==========##
##-------------algorithm: DE---------------------##
alg = QuanEstimation.DE(p_num=10, max_episode=1000, c=1.0, cr=0.5)
# input the dynamics data
dynamics = QuanEstimation.Lindblad(opt, tspan, rho0, H0, dH, Hc,
decay=decay, dyn_method=:Expm)
# objective function: CFI
obj = QuanEstimation.CFIM_obj()
# run the comprehensive optimization problem
QuanEstimation.run(opt, alg, obj, dynamics; savefile=false)
##-------------algorithm: PSO---------------------##
# alg = QuanEstimation.PSO(p_num=10, max_episode=[1000,100], c0=1.0,
# c1=2.0, c2=2.0)
# # input the dynamics data
# dynamics = QuanEstimation.Lindblad(opt, tspan, rho0, H0, dH, Hc,
# decay=decay, dyn_method=:Expm)
# # objective function: CFI
# obj = QuanEstimation.CFIM_obj()
# # run the comprehensive optimization problem
# QuanEstimation.run(opt, alg, obj, dynamics; savefile=false)
96 changes: 48 additions & 48 deletions examples/CMopt_qubit.jl
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using QuanEstimation

# initial state
rho0 = 0.5*ones(2, 2)
# free Hamiltonian
omega = 1.0
sx = [0. 1.; 1. 0.0im]
sy = [0. -im; im 0.]
sz = [1. 0.0im; 0. -1.]
H0 = 0.5*omega*sz
# derivative of the free Hamiltonian on omega
dH = [0.5*sz]
# control Hamiltonians
Hc = [sx, sy, sz]
# dissipation
sp = [0. 1.; 0. 0.0im]
sm = [0. 0.; 1. 0.0im]
decay = [[sp, 0.0], [sm, 0.1]]
# measurement
M1 = 0.5*[1.0+0.0im 1.; 1. 1.]
M2 = 0.5*[1.0+0.0im -1.; -1. 1.]
M = [M1, M2]
# time length for the evolution
tspan = range(0., 10., length=2500)
# control and measurement optimization
opt = QuanEstimation.CMopt(ctrl_bound=[-2.0,2.0], seed=1234)

##==========choose comprehensive optimization algorithm==========##
##-------------algorithm: DE---------------------##
alg = QuanEstimation.DE(p_num=10, max_episode=1000, c=1.0, cr=0.5)
# input the dynamics data
dynamics = QuanEstimation.Lindblad(opt, tspan, rho0, H0, dH, Hc,
decay=decay)
# objective function: CFI
obj = QuanEstimation.CFIM_obj()
# run the comprehensive optimization problem
QuanEstimation.run(opt, alg, obj, dynamics; savefile=false)

##-------------algorithm: PSO---------------------##
# alg = QuanEstimation.PSO(p_num=10, max_episode=[1000,100], c0=1.0,
# c1=2.0, c2=2.0)
# # input the dynamics data
# dynamics = QuanEstimation.Lindblad(opt, tspan, rho0, H0, dH, Hc,
# decay=decay)
# # objective function: CFI
# obj = QuanEstimation.CFIM_obj()
# # run the comprehensive optimization problem
# QuanEstimation.run(opt, alg, obj, dynamics; savefile=false)
using QuanEstimation
# initial state
rho0 = 0.5*ones(2, 2)
# free Hamiltonian
omega = 1.0
sx = [0. 1.; 1. 0.0im]
sy = [0. -im; im 0.]
sz = [1. 0.0im; 0. -1.]
H0 = 0.5*omega*sz
# derivative of the free Hamiltonian on omega
dH = [0.5*sz]
# control Hamiltonians
Hc = [sx, sy, sz]
# dissipation
sp = [0. 1.; 0. 0.0im]
sm = [0. 0.; 1. 0.0im]
decay = [[sp, 0.0], [sm, 0.1]]
# measurement
M1 = 0.5*[1.0+0.0im 1.; 1. 1.]
M2 = 0.5*[1.0+0.0im -1.; -1. 1.]
M = [M1, M2]
# time length for the evolution
tspan = range(0., 10., length=2500)
# control and measurement optimization
opt = QuanEstimation.CMopt(ctrl_bound=[-2.0,2.0], seed=1234)
##==========choose comprehensive optimization algorithm==========##
##-------------algorithm: DE---------------------##
alg = QuanEstimation.DE(p_num=10, max_episode=1000, c=1.0, cr=0.5)
# input the dynamics data
dynamics = QuanEstimation.Lindblad(opt, tspan, rho0, H0, dH, Hc,
decay=decay, dyn_method=:Expm)
# objective function: CFI
obj = QuanEstimation.CFIM_obj()
# run the comprehensive optimization problem
QuanEstimation.run(opt, alg, obj, dynamics; savefile=false)
##-------------algorithm: PSO---------------------##
# alg = QuanEstimation.PSO(p_num=10, max_episode=[1000,100], c0=1.0,
# c1=2.0, c2=2.0)
# # input the dynamics data
# dynamics = QuanEstimation.Lindblad(opt, tspan, rho0, H0, dH, Hc,
# decay=decay, dyn_method=:Expm)
# # objective function: CFI
# obj = QuanEstimation.CFIM_obj()
# # run the comprehensive optimization problem
# QuanEstimation.run(opt, alg, obj, dynamics; savefile=false)
122 changes: 61 additions & 61 deletions examples/SCMopt_NV.jl
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using QuanEstimation
using Random
using LinearAlgebra

# initial state
rho0 = zeros(ComplexF64, 6, 6)
rho0[1:4:5, 1:4:5] .= 0.5
# Hamiltonian
sx = [0. 1.; 1. 0.]
sy = [0. -im; im 0.]
sz = [1. 0.; 0. -1.]
s1 = [0. 1. 0.; 1. 0. 1.; 0. 1. 0.]/sqrt(2)
s2 = [0. -im 0.; im 0. -im; 0. im 0.]/sqrt(2)
s3 = [1. 0. 0.; 0. 0. 0.; 0. 0. -1.]
Is = I1, I2, I3 = [kron(I(3), sx), kron(I(3), sy), kron(I(3), sz)]
S = S1, S2, S3 = [kron(s1, I(2)), kron(s2, I(2)), kron(s3, I(2))]
B = B1, B2, B3 = [5.0e-4, 5.0e-4, 5.0e-4]
# All numbers are divided by 100 in this example
# for better calculation accurancy
cons = 100
D = (2pi*2.87*1000)/cons
gS = (2pi*28.03*1000)/cons
gI = (2pi*4.32)/cons
A1 = (2pi*3.65)/cons
A2 = (2pi*3.03)/cons
H0 = sum([D*kron(s3^2, I(2)), sum(gS*B.*S), sum(gI*B.*Is),
A1*(kron(s1, sx) + kron(s2, sy)), A2*kron(s3, sz)])
# derivatives of the free Hamiltonian on B1, B2 and B3
dH = gS*S+gI*Is
# control Hamiltonians
Hc = [S1, S2, S3]
# dissipation
decay = [[S3, 2pi/cons]]
# generation of a set of POVM basis
dim = size(rho0, 1)
POVM_basis = [QuanEstimation.basis(dim, i)*QuanEstimation.basis(dim, i)'
for i in 1:dim]
# time length for the evolution
tspan = range(0., 2., length=4000)
# choose the optimization type
opt = QuanEstimation.SCMopt(ctrl_bound=[-0.2,0.2], seed=1234)

##==========choose comprehensive optimization algorithm==========##
##-------------algorithm: DE---------------------##
alg = QuanEstimation.DE(p_num=10, max_episode=1000, c=1.0, cr=0.5)
# input the dynamics data
dynamics = QuanEstimation.Lindblad(opt, tspan, H0, dH, Hc, decay=decay)
# objective function: tr(WI^{-1})
obj = QuanEstimation.CFIM_obj()
# run the comprehensive optimization problem
QuanEstimation.run(opt, alg, obj, dynamics; savefile=false)

##-------------algorithm: PSO---------------------##
# alg = QuanEstimation.PSO(p_num=10, max_episode=[1000,100], c0=1.0,
# c1=2.0, c2=2.0)
# # input the dynamics data
# dynamics = QuanEstimation.Lindblad(opt, tspan, H0, dH, Hc, decay=decay)
# # objective function: tr(WI^{-1})
# obj = QuanEstimation.CFIM_obj()
# # run the comprehensive optimization problem
# QuanEstimation.run(opt, alg, obj, dynamics; savefile=false)
using QuanEstimation
using Random
using LinearAlgebra
# initial state
rho0 = zeros(ComplexF64, 6, 6)
rho0[1:4:5, 1:4:5] .= 0.5
# Hamiltonian
sx = [0. 1.; 1. 0.]
sy = [0. -im; im 0.]
sz = [1. 0.; 0. -1.]
s1 = [0. 1. 0.; 1. 0. 1.; 0. 1. 0.]/sqrt(2)
s2 = [0. -im 0.; im 0. -im; 0. im 0.]/sqrt(2)
s3 = [1. 0. 0.; 0. 0. 0.; 0. 0. -1.]
Is = I1, I2, I3 = [kron(I(3), sx), kron(I(3), sy), kron(I(3), sz)]
S = S1, S2, S3 = [kron(s1, I(2)), kron(s2, I(2)), kron(s3, I(2))]
B = B1, B2, B3 = [5.0e-4, 5.0e-4, 5.0e-4]
# All numbers are divided by 100 in this example
# for better calculation accurancy
cons = 100
D = (2pi*2.87*1000)/cons
gS = (2pi*28.03*1000)/cons
gI = (2pi*4.32)/cons
A1 = (2pi*3.65)/cons
A2 = (2pi*3.03)/cons
H0 = sum([D*kron(s3^2, I(2)), sum(gS*B.*S), sum(gI*B.*Is),
A1*(kron(s1, sx) + kron(s2, sy)), A2*kron(s3, sz)])
# derivatives of the free Hamiltonian on B1, B2 and B3
dH = gS*S+gI*Is
# control Hamiltonians
Hc = [S1, S2, S3]
# dissipation
decay = [[S3, 2pi/cons]]
# generation of a set of POVM basis
dim = size(rho0, 1)
POVM_basis = [QuanEstimation.basis(dim, i)*QuanEstimation.basis(dim, i)'
for i in 1:dim]
# time length for the evolution
tspan = range(0., 2., length=4000)
# choose the optimization type
opt = QuanEstimation.SCMopt(ctrl_bound=[-0.2,0.2], seed=1234)
##==========choose comprehensive optimization algorithm==========##
##-------------algorithm: DE---------------------##
alg = QuanEstimation.DE(p_num=10, max_episode=1000, c=1.0, cr=0.5)
# input the dynamics data
dynamics = QuanEstimation.Lindblad(opt, tspan, H0, dH, Hc, decay=decay, dyn_method=:Expm)
# objective function: tr(WI^{-1})
obj = QuanEstimation.CFIM_obj()
# run the comprehensive optimization problem
QuanEstimation.run(opt, alg, obj, dynamics; savefile=false)
##-------------algorithm: PSO---------------------##
# alg = QuanEstimation.PSO(p_num=10, max_episode=[1000,100], c0=1.0,
# c1=2.0, c2=2.0)
# # input the dynamics data
# dynamics = QuanEstimation.Lindblad(opt, tspan, H0, dH, Hc, decay=decay, dyn_method=:Expm)
# # objective function: tr(WI^{-1})
# obj = QuanEstimation.CFIM_obj()
# # run the comprehensive optimization problem
# QuanEstimation.run(opt, alg, obj, dynamics; savefile=false)
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