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‎examples/Bayesian_CramerRao_bounds.jl

-1
Original file line numberDiff line numberDiff line change
@@ -49,4 +49,3 @@ f_BQCRB2 = BQCRB(scheme; btype = 2)
4949
f_BQCRB3 = BQCRB(scheme; btype = 3)
5050
f_QVTB = QVTB(scheme)
5151
f_QZZB = QZZB(scheme)
52-

‎examples/Bayesian_estimation.jl

+2-2
Original file line numberDiff line numberDiff line change
@@ -1,10 +1,10 @@
11
using QuanEstimation, Random
22

33
function H0_func(x)
4-
return 0.5 * pi/2 * (σx() * cos(x) + σz() * sin(x))
4+
return 0.5 * pi / 2 * (σx() * cos(x) + σz() * sin(x))
55
end
66
function dH_func(x)
7-
return [0.5 * pi/2 * (-σx() * sin(x) + σz() * cos(x))]
7+
return [0.5 * pi / 2 * (-σx() * sin(x) + σz() * cos(x))]
88
end
99

1010
# initial state

‎examples/CMopt_qubit.jl

+1-1
Original file line numberDiff line numberDiff line change
@@ -40,4 +40,4 @@ scheme = GeneralScheme(; probe = rho0, param = dynamics)
4040
opt = CMopt(ctrl_bound = [-2.0, 2.0], seed = 1234)
4141

4242
# run the control optimization problem
43-
optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)
43+
optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)

‎examples/HCRB_NHB.jl

+2-2
Original file line numberDiff line numberDiff line change
@@ -23,10 +23,10 @@ W = one(zeros(2, 2))
2323
# time length for the evolution
2424
tspan = range(0.0, 5.0, length = 200)
2525
# dynamics
26-
rho, drho = expm(tspan, rho0, H0, dH; decay=decay)
26+
rho, drho = expm(tspan, rho0, H0, dH; decay = decay)
2727
# calculation of the CFIM, QFIM and HCRB
2828
f_HCRB, f_NHB = [], []
29-
for ti = eachindex(tspan)[2:end]
29+
for ti in eachindex(tspan)[2:end]
3030
# HCRB
3131
f_tp1 = HCRB(rho[ti], drho[ti], W)
3232
append!(f_HCRB, f_tp1)

‎examples/SCMopt_NV.jl

+1-1
Original file line numberDiff line numberDiff line change
@@ -54,4 +54,4 @@ obj = CFIM_obj()
5454

5555
opt = SCMopt(ctrl_bound = [-0.2, 0.2], seed = 1234)
5656

57-
optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)
57+
optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)

‎examples/SCMopt_qubit.jl

+1-1
Original file line numberDiff line numberDiff line change
@@ -38,4 +38,4 @@ obj = CFIM_obj()
3838

3939
opt = SCMopt(ctrl_bound = [-0.2, 0.2], seed = 1234)
4040

41-
optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)
41+
optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)

‎examples/SCopt_NV.jl

+1-1
Original file line numberDiff line numberDiff line change
@@ -63,4 +63,4 @@ scheme = GeneralScheme(; probe = rho0, param = dynamics)
6363

6464
opt = SCopt(ctrl_bound = [-0.2, 0.2], seed = 1234)
6565

66-
optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)
66+
optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)

‎examples/SCopt_qubit.jl

+1-1
Original file line numberDiff line numberDiff line change
@@ -47,4 +47,4 @@ scheme = GeneralScheme(; probe = rho0, param = dynamics)
4747

4848
opt = SCopt(ctrl_bound = [-0.2, 0.2], seed = 1234)
4949

50-
optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)
50+
optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)

‎examples/SMopt_NV.jl

+1-1
Original file line numberDiff line numberDiff line change
@@ -54,4 +54,4 @@ obj = CFIM_obj()
5454

5555
opt = SMopt(seed = 1234)
5656

57-
optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)
57+
optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)

‎examples/SMopt_qubit.jl

+1-1
Original file line numberDiff line numberDiff line change
@@ -38,4 +38,4 @@ obj = CFIM_obj()
3838

3939
opt = SMopt(seed = 1234)
4040

41-
optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)
41+
optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)

‎examples/measurement_optimization_rotation_NV.jl

+1-1
Original file line numberDiff line numberDiff line change
@@ -58,4 +58,4 @@ obj = CFIM_obj()
5858
# find the optimal rotated measurement of an input measurement
5959
opt = MeasurementOpt(mtype = :Rotation, POVM_basis = POVM_basis, seed = 1234)
6060

61-
optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)
61+
optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)

‎examples/state_optimization_LMG1.jl

+1-1
Original file line numberDiff line numberDiff line change
@@ -59,4 +59,4 @@ scheme = GeneralScheme(; probe = psi0, param = dynamics)
5959
# set the optimization type
6060
opt = StateOpt(psi = psi0, seed = 1234)
6161

62-
optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)
62+
optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)

‎examples/state_optimization_LMG2.jl

+1-1
Original file line numberDiff line numberDiff line change
@@ -62,4 +62,4 @@ scheme = GeneralScheme(; probe = psi0, param = dynamics)
6262
# set the optimization type
6363
opt = StateOpt(psi = psi0, seed = 1234)
6464

65-
optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)
65+
optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)

‎lib/NVMagnetometer/src/NVMagnetometer.jl

+11-5
Original file line numberDiff line numberDiff line change
@@ -33,9 +33,9 @@ struct NVMagnetometerData
3333
decay_opt::Vector{Matrix{ComplexF64}}##decay_operator
3434
init_state::Vector{ComplexF64}##ρ0
3535
Hc::Vector{Matrix{ComplexF64}} ##control_Hamiltonians
36-
ctrl::Union{Nothing, Vector{Vector{Float64}}} ##control_coefficients
37-
tspan::Union{Vector{Float64}, StepRangeLen} ##time_span
38-
M::Union{Nothing, Vector{Matrix{ComplexF64}}} ##meassurments
36+
ctrl::Union{Nothing,Vector{Vector{Float64}}} ##control_coefficients
37+
tspan::Union{Vector{Float64},StepRangeLen} ##time_span
38+
M::Union{Nothing,Vector{Matrix{ComplexF64}}} ##meassurments
3939
end
4040

4141
# Base.keys(t::NVMagnetometer{names...}) where {names...} = [names...]
@@ -165,15 +165,21 @@ QuanEstimationBase.CFIM(nv::NVMagnetometerScheme; kwargs...) =
165165
CFIM(getscheme(nv.data); kwargs...)
166166
QuanEstimationBase.HCRB(nv::NVMagnetometerScheme; kwargs...) =
167167
HCRB(getscheme(nv.data); kwargs...)
168-
168+
169169
function QuanEstimationBase.optimize!(
170170
nv::NVMagnetometerScheme,
171171
opt;
172172
algorithm = autoGRAPE(),
173173
objective = QFIM_obj(),
174174
savefile = false,
175175
)
176-
QuanEstimationBase.optimize!(getscheme(nv.data), opt; algorithm = algorithm, objective = objective, savefile = savefile)
176+
QuanEstimationBase.optimize!(
177+
getscheme(nv.data),
178+
opt;
179+
algorithm = algorithm,
180+
objective = objective,
181+
savefile = savefile,
182+
)
177183
end
178184

179185

‎lib/QuanEstimationBase/ext/QuanEstimationBasePyExt.jl

+8-4
Original file line numberDiff line numberDiff line change
@@ -208,8 +208,10 @@ function QuanEstimationBase.ode_py(
208208
)
209209
ctrl_num = length(Hc)
210210
ctrl_interval = ((length(tspan) - 1) / length(ctrl[1])) |> Int
211-
ctrl =
212-
[repeat_copy(ctrl[i], 1, ctrl_interval) |> transpose |> vec |> Array for i = 1:ctrl_num]
211+
ctrl = [
212+
repeat_copy(ctrl[i], 1, ctrl_interval) |> transpose |> vec |> Array for
213+
i = 1:ctrl_num
214+
]
213215
push!.(ctrl, [0.0 for i = 1:ctrl_num])
214216
H(ctrl) = Htot(H0, Hc, ctrl)
215217
dt = tspan[2] - tspan[1]
@@ -251,8 +253,10 @@ function QuanEstimationBase.ode_py(
251253
param_num = length(dH)
252254
ctrl_num = length(Hc)
253255
ctrl_interval = ((length(tspan) - 1) / length(ctrl[1])) |> Int
254-
ctrl =
255-
[repeat_copy(ctrl[i], 1, ctrl_interval) |> transpose |> vec |> Array for i = 1:ctrl_num]
256+
ctrl = [
257+
repeat_copy(ctrl[i], 1, ctrl_interval) |> transpose |> vec |> Array for
258+
i = 1:ctrl_num
259+
]
256260
push!.(ctrl, [0.0 for i = 1:ctrl_num])
257261
H(ctrl) = Htot(H0, Hc, ctrl)
258262
dt = tspan[2] - tspan[1]

‎lib/QuanEstimationBase/src/Algorithm/DE.jl

+1-2
Original file line numberDiff line numberDiff line change
@@ -246,8 +246,7 @@ function optimize!(opt::Mopt_LinearComb, alg::DE, obj, scheme, output)
246246
p_fit, p_out = zeros(p_num), zeros(p_num)
247247
for pj = 1:p_num
248248
M = [
249-
sum([populations[pj][i][j] * POVM_basis[j] for j = 1:basis_num]) for
250-
i = 1:M_num
249+
sum([populations[pj][i][j] * POVM_basis[j] for j = 1:basis_num]) for i = 1:M_num
251250
]
252251
obj_copy = set_M(obj, M)
253252
p_out[pj], p_fit[pj] = objective(obj_copy, scheme)

‎lib/QuanEstimationBase/src/Algorithm/GRAPE.jl

+101-101
Original file line numberDiff line numberDiff line change
@@ -53,7 +53,7 @@ end
5353
function scheme_analy(scheme, dim, tnum, para_num, ctrl_num)
5454
pdata = param_data(scheme)
5555
rho0 = state_data(scheme)
56-
sdata = ndims(rho0)[1]==1 ? rho0*rho0' : rho0
56+
sdata = ndims(rho0)[1] == 1 ? rho0 * rho0' : rho0
5757

5858
tspan = pdata.tspan
5959
Δt = tspan[2] - tspan[1]
@@ -245,7 +245,7 @@ function gradient_QFIM_analy(alg::GRAPE, obj, scheme)
245245
dim = get_dim(scheme)
246246
tnum = length(pdata.tspan)
247247
para_num = length(pdata.hamiltonian.dH)
248-
ctrl_num = get_ctrl_num(scheme)
248+
ctrl_num = get_ctrl_num(scheme)
249249

250250
ρt_T, ∂ρt_T, δρt_δV, ∂xδρt_δV = scheme_analy(scheme, dim, tnum, para_num, ctrl_num)
251251

@@ -266,43 +266,43 @@ function gradient_QFIM_analy(alg::GRAPE, obj, scheme)
266266
pdata.ctrl[cm][tm] = pdata.ctrl[cm][tm] + alg.epsilon * δF
267267
end
268268
end
269-
# elseif para_num == 2
270-
# coeff1 = real(det(F))
271-
# coeff2 =
272-
# obj.W[1, 1] * F_T[2, 2] + obj.W[2, 2] * F_T[1, 1] - obj.W[1, 2] * F_T[2, 1] -
273-
# obj.W[2, 1] * F_T[1, 2]
274-
# cost_function =
275-
# (abs(det(F_T)) < obj.eps ? (1.0 / obj.eps) : real(tr(obj.W * inv(F_T))))
276-
# for cm = 1:ctrl_num
277-
# for tm = 1:(tnum-1)
278-
# δF_all = [[0.0 for i = 1:para_num] for j = 1:para_num]
279-
# ∂ρt_T_δV = δρt_δV[cm][tm] |> vec2mat
280-
# for pm = 1:para_num
281-
# for pn = 1:para_num
282-
# ∂xδρt_T_δV_a = ∂xδρt_δV[pm][cm][tm] |> vec2mat
283-
# ∂xδρt_T_δV_b = ∂xδρt_δV[pn][cm][tm] |> vec2mat
284-
# term1 = tr(∂xδρt_T_δV_a * Lx[pn])
285-
# term2 = tr(∂xδρt_T_δV_b * Lx[pm])
286-
287-
# anti_commu = Lx[pm] * Lx[pn] + Lx[pn] * Lx[pm]
288-
# term2 = tr(∂ρt_T_δV * anti_commu)
289-
# δF_all[pm][pn] = ((2 * term1 - 0.5 * term2) |> real)
290-
# end
291-
# end
292-
# item1 =
293-
# -coeff2 * (
294-
# F_T[2, 2] * δF_all[1][1] + F_T[1, 1] * δF_all[2][2] -
295-
# F_T[2, 1] * δF_all[1][2] - F_T[1, 2] * δF_all[2][1]
296-
# ) / coeff1^2
297-
# item2 =
298-
# (
299-
# obj.W[1, 1] * δF_all[2][2] + obj.W[2, 2] * δF_all[1][1] -
300-
# obj.W[1, 2] * δF_all[2][1] - obj.W[2, 1] * δF_all[1][2]
301-
# ) / coeff1
302-
# δF = -(item1 + item2) * cost_function^2
303-
# pdata.ctrl[cm][tm] = pdata.ctrl[cm][tm] + alg.epsilon * δF
304-
# end
305-
# end
269+
# elseif para_num == 2
270+
# coeff1 = real(det(F))
271+
# coeff2 =
272+
# obj.W[1, 1] * F_T[2, 2] + obj.W[2, 2] * F_T[1, 1] - obj.W[1, 2] * F_T[2, 1] -
273+
# obj.W[2, 1] * F_T[1, 2]
274+
# cost_function =
275+
# (abs(det(F_T)) < obj.eps ? (1.0 / obj.eps) : real(tr(obj.W * inv(F_T))))
276+
# for cm = 1:ctrl_num
277+
# for tm = 1:(tnum-1)
278+
# δF_all = [[0.0 for i = 1:para_num] for j = 1:para_num]
279+
# ∂ρt_T_δV = δρt_δV[cm][tm] |> vec2mat
280+
# for pm = 1:para_num
281+
# for pn = 1:para_num
282+
# ∂xδρt_T_δV_a = ∂xδρt_δV[pm][cm][tm] |> vec2mat
283+
# ∂xδρt_T_δV_b = ∂xδρt_δV[pn][cm][tm] |> vec2mat
284+
# term1 = tr(∂xδρt_T_δV_a * Lx[pn])
285+
# term2 = tr(∂xδρt_T_δV_b * Lx[pm])
286+
287+
# anti_commu = Lx[pm] * Lx[pn] + Lx[pn] * Lx[pm]
288+
# term2 = tr(∂ρt_T_δV * anti_commu)
289+
# δF_all[pm][pn] = ((2 * term1 - 0.5 * term2) |> real)
290+
# end
291+
# end
292+
# item1 =
293+
# -coeff2 * (
294+
# F_T[2, 2] * δF_all[1][1] + F_T[1, 1] * δF_all[2][2] -
295+
# F_T[2, 1] * δF_all[1][2] - F_T[1, 2] * δF_all[2][1]
296+
# ) / coeff1^2
297+
# item2 =
298+
# (
299+
# obj.W[1, 1] * δF_all[2][2] + obj.W[2, 2] * δF_all[1][1] -
300+
# obj.W[1, 2] * δF_all[2][1] - obj.W[2, 1] * δF_all[1][2]
301+
# ) / coeff1
302+
# δF = -(item1 + item2) * cost_function^2
303+
# pdata.ctrl[cm][tm] = pdata.ctrl[cm][tm] + alg.epsilon * δF
304+
# end
305+
# end
306306
else
307307
cost_function =
308308
(abs(det(F_T)) < obj.eps ? (1.0 / obj.eps) : real(tr(obj.W * inv(F_T))))
@@ -365,68 +365,68 @@ function gradient_CFIM_analy(alg, obj, scheme)
365365
pdata.ctrl[cm][tm] = pdata.ctrl[cm][tm] + alg.epsilon * δF
366366
end
367367
end
368-
# elseif para_num == 2
369-
# F_T = CFIM(ρt_T, ∂ρt_T, obj.M; eps = obj.eps)
370-
# L1_tidle = [zeros(ComplexF64, dim, dim) for i = 1:para_num]
371-
# L2_tidle = [[zeros(ComplexF64, dim, dim) for i = 1:para_num] for j = 1:para_num]
372-
373-
# for para_i = 1:para_num
374-
# for mi = 1:dim
375-
# p = (tr(ρt_T * obj.M[mi]) |> real)
376-
# dp = (tr(∂ρt_T[para_i] * obj.M[mi]) |> real)
377-
# if p > obj.eps
378-
# L1_tidle[para_i] = L1_tidle[para_i] + dp * obj.M[mi] / p
379-
# end
380-
# end
381-
# end
382-
383-
# for para_i = 1:para_num
384-
# dp_a = (tr(∂ρt_T[para_i] * obj.M[mi]) |> real)
385-
# for para_j = 1:para_num
386-
# dp_b = (tr(∂ρt_T[para_j] * obj.M[mi]) |> real)
387-
# for mi = 1:dim
388-
# p = (tr(ρt_T * obj.M[mi]) |> real)
389-
# if p > obj.eps
390-
# L2_tidle[para_i][para_j] =
391-
# L2_tidle[para_i][para_j] + dp_a * dp_b * obj.M[mi] / p^2
392-
# end
393-
# end
394-
# end
395-
# end
396-
# coeff1 = real(det(F))
397-
# coeff2 =
398-
# obj.W[1, 1] * F_T[2, 2] + obj.W[2, 2] * F_T[1, 1] - obj.W[1, 2] * F_T[2, 1] -
399-
# obj.W[2, 1] * F_T[1, 2]
400-
# cost_function =
401-
# (abs(det(F_T)) < obj.eps ? (1.0 / obj.eps) : real(tr(obj.W * inv(F_T))))
402-
# for cm = 1:ctrl_num
403-
# for tm = 1:(tnum-1)
404-
# δF_all = [[0.0 for i = 1:para_num] for j = 1:para_num]
405-
# ∂ρt_T_δV = δρt_δV[cm][tm] |> vec2mat
406-
# for pm = 1:para_num
407-
# for pn = 1:para_num
408-
# ∂xδρt_T_δV_a = ∂xδρt_δV[pm][cm][tm] |> vec2mat
409-
# ∂xδρt_T_δV_b = ∂xδρt_δV[pn][cm][tm] |> vec2mat
410-
# term1 = tr(∂xδρt_T_δV_a * L1_tidle[pn])
411-
# term2 = tr(∂xδρt_T_δV_b * L1_tidle[pm])
412-
# term3 = tr(∂ρt_T_δV * L2_tidle[pm][pn])
413-
# δF_all[pm][pn] = ((term1 + term2 - term3) |> real)
414-
# end
415-
# end
416-
# item1 =
417-
# -coeff2 * (
418-
# F_T[2, 2] * δF_all[1][1] + F_T[1, 1] * δF_all[2][2] -
419-
# F_T[2, 1] * δF_all[1][2] - F_T[1, 2] * δF_all[2][1]
420-
# ) / coeff1^2
421-
# item2 =
422-
# (
423-
# obj.W[1, 1] * δF_all[2][2] + obj.W[2, 2] * δF_all[1][1] -
424-
# obj.W[1, 2] * δF_all[2][1] - obj.W[2, 1] * δF_all[1][2]
425-
# ) / coeff1
426-
# δF = -(item1 + item2) * cost_function^2
427-
# pdata.ctrl[cm][tm] = pdata.ctrl[cm][tm] + alg.epsilon * δF
428-
# end
429-
# end
368+
# elseif para_num == 2
369+
# F_T = CFIM(ρt_T, ∂ρt_T, obj.M; eps = obj.eps)
370+
# L1_tidle = [zeros(ComplexF64, dim, dim) for i = 1:para_num]
371+
# L2_tidle = [[zeros(ComplexF64, dim, dim) for i = 1:para_num] for j = 1:para_num]
372+
373+
# for para_i = 1:para_num
374+
# for mi = 1:dim
375+
# p = (tr(ρt_T * obj.M[mi]) |> real)
376+
# dp = (tr(∂ρt_T[para_i] * obj.M[mi]) |> real)
377+
# if p > obj.eps
378+
# L1_tidle[para_i] = L1_tidle[para_i] + dp * obj.M[mi] / p
379+
# end
380+
# end
381+
# end
382+
383+
# for para_i = 1:para_num
384+
# dp_a = (tr(∂ρt_T[para_i] * obj.M[mi]) |> real)
385+
# for para_j = 1:para_num
386+
# dp_b = (tr(∂ρt_T[para_j] * obj.M[mi]) |> real)
387+
# for mi = 1:dim
388+
# p = (tr(ρt_T * obj.M[mi]) |> real)
389+
# if p > obj.eps
390+
# L2_tidle[para_i][para_j] =
391+
# L2_tidle[para_i][para_j] + dp_a * dp_b * obj.M[mi] / p^2
392+
# end
393+
# end
394+
# end
395+
# end
396+
# coeff1 = real(det(F))
397+
# coeff2 =
398+
# obj.W[1, 1] * F_T[2, 2] + obj.W[2, 2] * F_T[1, 1] - obj.W[1, 2] * F_T[2, 1] -
399+
# obj.W[2, 1] * F_T[1, 2]
400+
# cost_function =
401+
# (abs(det(F_T)) < obj.eps ? (1.0 / obj.eps) : real(tr(obj.W * inv(F_T))))
402+
# for cm = 1:ctrl_num
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# for tm = 1:(tnum-1)
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# δF_all = [[0.0 for i = 1:para_num] for j = 1:para_num]
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# ∂ρt_T_δV = δρt_δV[cm][tm] |> vec2mat
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# for pm = 1:para_num
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# for pn = 1:para_num
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# ∂xδρt_T_δV_a = ∂xδρt_δV[pm][cm][tm] |> vec2mat
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# ∂xδρt_T_δV_b = ∂xδρt_δV[pn][cm][tm] |> vec2mat
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# term1 = tr(∂xδρt_T_δV_a * L1_tidle[pn])
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# term2 = tr(∂xδρt_T_δV_b * L1_tidle[pm])
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# term3 = tr(∂ρt_T_δV * L2_tidle[pm][pn])
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# δF_all[pm][pn] = ((term1 + term2 - term3) |> real)
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# end
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# end
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# item1 =
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# -coeff2 * (
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# F_T[2, 2] * δF_all[1][1] + F_T[1, 1] * δF_all[2][2] -
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# F_T[2, 1] * δF_all[1][2] - F_T[1, 2] * δF_all[2][1]
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# ) / coeff1^2
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# item2 =
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# (
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# obj.W[1, 1] * δF_all[2][2] + obj.W[2, 2] * δF_all[1][1] -
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# obj.W[1, 2] * δF_all[2][1] - obj.W[2, 1] * δF_all[1][2]
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# ) / coeff1
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# δF = -(item1 + item2) * cost_function^2
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# pdata.ctrl[cm][tm] = pdata.ctrl[cm][tm] + alg.epsilon * δF
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# end
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# end
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else
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F_T = CFIM(ρt_T, ∂ρt_T, obj.M; eps = obj.eps)
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L1_tidle = [zeros(ComplexF64, dim, dim) for i = 1:para_num]

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