Format .jl files

examples/Bayesian_CramerRao_bounds.jl

@@ -49,4 +49,3 @@ f_BQCRB2 = BQCRB(scheme; btype = 2)
 f_BQCRB3 = BQCRB(scheme; btype = 3)
 f_QVTB = QVTB(scheme)
 f_QZZB = QZZB(scheme)
-

examples/Bayesian_estimation.jl

@@ -1,10 +1,10 @@
 using QuanEstimation, Random
 
 function H0_func(x)
-    return 0.5 * pi/2 * (σx() * cos(x) + σz() * sin(x))
+    return 0.5 * pi / 2 * (σx() * cos(x) + σz() * sin(x))
 end
 function dH_func(x)
-    return [0.5 * pi/2 * (-σx() * sin(x) + σz() * cos(x))]
+    return [0.5 * pi / 2 * (-σx() * sin(x) + σz() * cos(x))]
 end
 
 # initial state

examples/CMopt_qubit.jl

@@ -40,4 +40,4 @@ scheme = GeneralScheme(; probe = rho0, param = dynamics)
 opt = CMopt(ctrl_bound = [-2.0, 2.0], seed = 1234)
 
 # run the control optimization problem
-optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)
+optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)

examples/HCRB_NHB.jl

@@ -23,10 +23,10 @@ W = one(zeros(2, 2))
 # time length for the evolution
 tspan = range(0.0, 5.0, length = 200)
 # dynamics
-rho, drho = expm(tspan, rho0, H0, dH; decay=decay)
+rho, drho = expm(tspan, rho0, H0, dH; decay = decay)
 # calculation of the CFIM, QFIM and HCRB
 f_HCRB, f_NHB = [], []
-for ti = eachindex(tspan)[2:end]
+for ti in eachindex(tspan)[2:end]
     # HCRB
     f_tp1 = HCRB(rho[ti], drho[ti], W)
     append!(f_HCRB, f_tp1)

examples/SCMopt_NV.jl

@@ -54,4 +54,4 @@ obj = CFIM_obj()
 
 opt = SCMopt(ctrl_bound = [-0.2, 0.2], seed = 1234)
 
-optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)
+optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)

examples/SCMopt_qubit.jl

@@ -38,4 +38,4 @@ obj = CFIM_obj()
 
 opt = SCMopt(ctrl_bound = [-0.2, 0.2], seed = 1234)
 
-optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)
+optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)

examples/SCopt_NV.jl

@@ -63,4 +63,4 @@ scheme = GeneralScheme(; probe = rho0, param = dynamics)
 
 opt = SCopt(ctrl_bound = [-0.2, 0.2], seed = 1234)
 
-optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)
+optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)

examples/SCopt_qubit.jl

@@ -47,4 +47,4 @@ scheme = GeneralScheme(; probe = rho0, param = dynamics)
 
 opt = SCopt(ctrl_bound = [-0.2, 0.2], seed = 1234)
 
-optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)
+optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)

examples/SMopt_NV.jl

@@ -54,4 +54,4 @@ obj = CFIM_obj()
 
 opt = SMopt(seed = 1234)
 
-optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)
+optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)

examples/SMopt_qubit.jl

@@ -38,4 +38,4 @@ obj = CFIM_obj()
 
 opt = SMopt(seed = 1234)
 
-optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)
+optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)

examples/measurement_optimization_rotation_NV.jl

@@ -58,4 +58,4 @@ obj = CFIM_obj()
 # find the optimal rotated measurement of an input measurement
 opt = MeasurementOpt(mtype = :Rotation, POVM_basis = POVM_basis, seed = 1234)
 
-optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)
+optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)

examples/state_optimization_LMG1.jl

@@ -59,4 +59,4 @@ scheme = GeneralScheme(; probe = psi0, param = dynamics)
 # set the optimization type
 opt = StateOpt(psi = psi0, seed = 1234)
 
-optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)
+optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)

examples/state_optimization_LMG2.jl

@@ -62,4 +62,4 @@ scheme = GeneralScheme(; probe = psi0, param = dynamics)
 # set the optimization type
 opt = StateOpt(psi = psi0, seed = 1234)
 
-optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)
+optimize!(scheme, opt; algorithm = alg, objective = obj, savefile = false)

lib/NVMagnetometer/src/NVMagnetometer.jl

@@ -33,9 +33,9 @@ struct NVMagnetometerData
     decay_opt::Vector{Matrix{ComplexF64}}##decay_operator
     init_state::Vector{ComplexF64}##ρ0
     Hc::Vector{Matrix{ComplexF64}} ##control_Hamiltonians
-    ctrl::Union{Nothing, Vector{Vector{Float64}}} ##control_coefficients
-    tspan::Union{Vector{Float64}, StepRangeLen} ##time_span
-    M::Union{Nothing, Vector{Matrix{ComplexF64}}} ##meassurments
+    ctrl::Union{Nothing,Vector{Vector{Float64}}} ##control_coefficients
+    tspan::Union{Vector{Float64},StepRangeLen} ##time_span
+    M::Union{Nothing,Vector{Matrix{ComplexF64}}} ##meassurments
 end
 
 # Base.keys(t::NVMagnetometer{names...}) where {names...} = [names...]
@@ -165,15 +165,21 @@ QuanEstimationBase.CFIM(nv::NVMagnetometerScheme; kwargs...) =
     CFIM(getscheme(nv.data); kwargs...)
 QuanEstimationBase.HCRB(nv::NVMagnetometerScheme; kwargs...) =
     HCRB(getscheme(nv.data); kwargs...)
-    
+
 function QuanEstimationBase.optimize!(
     nv::NVMagnetometerScheme,
     opt;
     algorithm = autoGRAPE(),
     objective = QFIM_obj(),
     savefile = false,
 )
-    QuanEstimationBase.optimize!(getscheme(nv.data), opt; algorithm = algorithm, objective = objective, savefile = savefile)
+    QuanEstimationBase.optimize!(
+        getscheme(nv.data),
+        opt;
+        algorithm = algorithm,
+        objective = objective,
+        savefile = savefile,
+    )
 end
 
 

lib/QuanEstimationBase/ext/QuanEstimationBasePyExt.jl

@@ -208,8 +208,10 @@ function QuanEstimationBase.ode_py(
 )
     ctrl_num = length(Hc)
     ctrl_interval = ((length(tspan) - 1) / length(ctrl[1])) |> Int
-    ctrl =
-        [repeat_copy(ctrl[i], 1, ctrl_interval) |> transpose |> vec |> Array for i = 1:ctrl_num]
+    ctrl = [
+        repeat_copy(ctrl[i], 1, ctrl_interval) |> transpose |> vec |> Array for
+        i = 1:ctrl_num
+    ]
     push!.(ctrl, [0.0 for i = 1:ctrl_num])
     H(ctrl) = Htot(H0, Hc, ctrl)
     dt = tspan[2] - tspan[1]
@@ -251,8 +253,10 @@ function QuanEstimationBase.ode_py(
     param_num = length(dH)
     ctrl_num = length(Hc)
     ctrl_interval = ((length(tspan) - 1) / length(ctrl[1])) |> Int
-    ctrl =
-        [repeat_copy(ctrl[i], 1, ctrl_interval) |> transpose |> vec |> Array for i = 1:ctrl_num]
+    ctrl = [
+        repeat_copy(ctrl[i], 1, ctrl_interval) |> transpose |> vec |> Array for
+        i = 1:ctrl_num
+    ]
     push!.(ctrl, [0.0 for i = 1:ctrl_num])
     H(ctrl) = Htot(H0, Hc, ctrl)
     dt = tspan[2] - tspan[1]

lib/QuanEstimationBase/src/Algorithm/DE.jl

@@ -246,8 +246,7 @@ function optimize!(opt::Mopt_LinearComb, alg::DE, obj, scheme, output)
     p_fit, p_out = zeros(p_num), zeros(p_num)
     for pj = 1:p_num
         M = [
-            sum([populations[pj][i][j] * POVM_basis[j] for j = 1:basis_num]) for
-            i = 1:M_num
+            sum([populations[pj][i][j] * POVM_basis[j] for j = 1:basis_num]) for i = 1:M_num
         ]
         obj_copy = set_M(obj, M)
         p_out[pj], p_fit[pj] = objective(obj_copy, scheme)

lib/QuanEstimationBase/src/Algorithm/GRAPE.jl

@@ -53,7 +53,7 @@ end
 function scheme_analy(scheme, dim, tnum, para_num, ctrl_num)
     pdata = param_data(scheme)
     rho0 = state_data(scheme)
-    sdata = ndims(rho0)[1]==1 ? rho0*rho0' : rho0
+    sdata = ndims(rho0)[1] == 1 ? rho0 * rho0' : rho0
 
     tspan = pdata.tspan
     Δt = tspan[2] - tspan[1]
@@ -245,7 +245,7 @@ function gradient_QFIM_analy(alg::GRAPE, obj, scheme)
     dim = get_dim(scheme)
     tnum = length(pdata.tspan)
     para_num = length(pdata.hamiltonian.dH)
-    ctrl_num = get_ctrl_num(scheme) 
+    ctrl_num = get_ctrl_num(scheme)
 
     ρt_T, ∂ρt_T, δρt_δV, ∂xδρt_δV = scheme_analy(scheme, dim, tnum, para_num, ctrl_num)
 
@@ -266,43 +266,43 @@ function gradient_QFIM_analy(alg::GRAPE, obj, scheme)
                 pdata.ctrl[cm][tm] = pdata.ctrl[cm][tm] + alg.epsilon * δF
             end
         end
-    # elseif para_num == 2
-    #     coeff1 = real(det(F))
-    #     coeff2 =
-    #         obj.W[1, 1] * F_T[2, 2] + obj.W[2, 2] * F_T[1, 1] - obj.W[1, 2] * F_T[2, 1] -
-    #         obj.W[2, 1] * F_T[1, 2]
-    #     cost_function =
-    #         (abs(det(F_T)) < obj.eps ? (1.0 / obj.eps) : real(tr(obj.W * inv(F_T))))
-    #     for cm = 1:ctrl_num
-    #         for tm = 1:(tnum-1)
-    #             δF_all = [[0.0 for i = 1:para_num] for j = 1:para_num]
-    #             ∂ρt_T_δV = δρt_δV[cm][tm] |> vec2mat
-    #             for pm = 1:para_num
-    #                 for pn = 1:para_num
-    #                     ∂xδρt_T_δV_a = ∂xδρt_δV[pm][cm][tm] |> vec2mat
-    #                     ∂xδρt_T_δV_b = ∂xδρt_δV[pn][cm][tm] |> vec2mat
-    #                     term1 = tr(∂xδρt_T_δV_a * Lx[pn])
-    #                     term2 = tr(∂xδρt_T_δV_b * Lx[pm])
-
-    #                     anti_commu = Lx[pm] * Lx[pn] + Lx[pn] * Lx[pm]
-    #                     term2 = tr(∂ρt_T_δV * anti_commu)
-    #                     δF_all[pm][pn] = ((2 * term1 - 0.5 * term2) |> real)
-    #                 end
-    #             end
-    #             item1 =
-    #                 -coeff2 * (
-    #                     F_T[2, 2] * δF_all[1][1] + F_T[1, 1] * δF_all[2][2] -
-    #                     F_T[2, 1] * δF_all[1][2] - F_T[1, 2] * δF_all[2][1]
-    #                 ) / coeff1^2
-    #             item2 =
-    #                 (
-    #                     obj.W[1, 1] * δF_all[2][2] + obj.W[2, 2] * δF_all[1][1] -
-    #                     obj.W[1, 2] * δF_all[2][1] - obj.W[2, 1] * δF_all[1][2]
-    #                 ) / coeff1
-    #             δF = -(item1 + item2) * cost_function^2
-    #             pdata.ctrl[cm][tm] = pdata.ctrl[cm][tm] + alg.epsilon * δF
-    #         end
-    #     end
+        # elseif para_num == 2
+        #     coeff1 = real(det(F))
+        #     coeff2 =
+        #         obj.W[1, 1] * F_T[2, 2] + obj.W[2, 2] * F_T[1, 1] - obj.W[1, 2] * F_T[2, 1] -
+        #         obj.W[2, 1] * F_T[1, 2]
+        #     cost_function =
+        #         (abs(det(F_T)) < obj.eps ? (1.0 / obj.eps) : real(tr(obj.W * inv(F_T))))
+        #     for cm = 1:ctrl_num
+        #         for tm = 1:(tnum-1)
+        #             δF_all = [[0.0 for i = 1:para_num] for j = 1:para_num]
+        #             ∂ρt_T_δV = δρt_δV[cm][tm] |> vec2mat
+        #             for pm = 1:para_num
+        #                 for pn = 1:para_num
+        #                     ∂xδρt_T_δV_a = ∂xδρt_δV[pm][cm][tm] |> vec2mat
+        #                     ∂xδρt_T_δV_b = ∂xδρt_δV[pn][cm][tm] |> vec2mat
+        #                     term1 = tr(∂xδρt_T_δV_a * Lx[pn])
+        #                     term2 = tr(∂xδρt_T_δV_b * Lx[pm])
+
+        #                     anti_commu = Lx[pm] * Lx[pn] + Lx[pn] * Lx[pm]
+        #                     term2 = tr(∂ρt_T_δV * anti_commu)
+        #                     δF_all[pm][pn] = ((2 * term1 - 0.5 * term2) |> real)
+        #                 end
+        #             end
+        #             item1 =
+        #                 -coeff2 * (
+        #                     F_T[2, 2] * δF_all[1][1] + F_T[1, 1] * δF_all[2][2] -
+        #                     F_T[2, 1] * δF_all[1][2] - F_T[1, 2] * δF_all[2][1]
+        #                 ) / coeff1^2
+        #             item2 =
+        #                 (
+        #                     obj.W[1, 1] * δF_all[2][2] + obj.W[2, 2] * δF_all[1][1] -
+        #                     obj.W[1, 2] * δF_all[2][1] - obj.W[2, 1] * δF_all[1][2]
+        #                 ) / coeff1
+        #             δF = -(item1 + item2) * cost_function^2
+        #             pdata.ctrl[cm][tm] = pdata.ctrl[cm][tm] + alg.epsilon * δF
+        #         end
+        #     end
     else
         cost_function =
             (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)
                 pdata.ctrl[cm][tm] = pdata.ctrl[cm][tm] + alg.epsilon * δF
             end
         end
-    # elseif para_num == 2
-    #     F_T = CFIM(ρt_T, ∂ρt_T, obj.M; eps = obj.eps)
-    #     L1_tidle = [zeros(ComplexF64, dim, dim) for i = 1:para_num]
-    #     L2_tidle = [[zeros(ComplexF64, dim, dim) for i = 1:para_num] for j = 1:para_num]
-
-    #     for para_i = 1:para_num
-    #         for mi = 1:dim
-    #             p = (tr(ρt_T * obj.M[mi]) |> real)
-    #             dp = (tr(∂ρt_T[para_i] * obj.M[mi]) |> real)
-    #             if p > obj.eps
-    #                 L1_tidle[para_i] = L1_tidle[para_i] + dp * obj.M[mi] / p
-    #             end
-    #         end
-    #     end
-
-    #     for para_i = 1:para_num
-    #         dp_a = (tr(∂ρt_T[para_i] * obj.M[mi]) |> real)
-    #         for para_j = 1:para_num
-    #             dp_b = (tr(∂ρt_T[para_j] * obj.M[mi]) |> real)
-    #             for mi = 1:dim
-    #                 p = (tr(ρt_T * obj.M[mi]) |> real)
-    #                 if p > obj.eps
-    #                     L2_tidle[para_i][para_j] =
-    #                         L2_tidle[para_i][para_j] + dp_a * dp_b * obj.M[mi] / p^2
-    #                 end
-    #             end
-    #         end
-    #     end
-    #     coeff1 = real(det(F))
-    #     coeff2 =
-    #         obj.W[1, 1] * F_T[2, 2] + obj.W[2, 2] * F_T[1, 1] - obj.W[1, 2] * F_T[2, 1] -
-    #         obj.W[2, 1] * F_T[1, 2]
-    #     cost_function =
-    #         (abs(det(F_T)) < obj.eps ? (1.0 / obj.eps) : real(tr(obj.W * inv(F_T))))
-    #     for cm = 1:ctrl_num
-    #         for tm = 1:(tnum-1)
-    #             δF_all = [[0.0 for i = 1:para_num] for j = 1:para_num]
-    #             ∂ρt_T_δV = δρt_δV[cm][tm] |> vec2mat
-    #             for pm = 1:para_num
-    #                 for pn = 1:para_num
-    #                     ∂xδρt_T_δV_a = ∂xδρt_δV[pm][cm][tm] |> vec2mat
-    #                     ∂xδρt_T_δV_b = ∂xδρt_δV[pn][cm][tm] |> vec2mat
-    #                     term1 = tr(∂xδρt_T_δV_a * L1_tidle[pn])
-    #                     term2 = tr(∂xδρt_T_δV_b * L1_tidle[pm])
-    #                     term3 = tr(∂ρt_T_δV * L2_tidle[pm][pn])
-    #                     δF_all[pm][pn] = ((term1 + term2 - term3) |> real)
-    #                 end
-    #             end
-    #             item1 =
-    #                 -coeff2 * (
-    #                     F_T[2, 2] * δF_all[1][1] + F_T[1, 1] * δF_all[2][2] -
-    #                     F_T[2, 1] * δF_all[1][2] - F_T[1, 2] * δF_all[2][1]
-    #                 ) / coeff1^2
-    #             item2 =
-    #                 (
-    #                     obj.W[1, 1] * δF_all[2][2] + obj.W[2, 2] * δF_all[1][1] -
-    #                     obj.W[1, 2] * δF_all[2][1] - obj.W[2, 1] * δF_all[1][2]
-    #                 ) / coeff1
-    #             δF = -(item1 + item2) * cost_function^2
-    #             pdata.ctrl[cm][tm] = pdata.ctrl[cm][tm] + alg.epsilon * δF
-    #         end
-    #     end
+        # elseif para_num == 2
+        #     F_T = CFIM(ρt_T, ∂ρt_T, obj.M; eps = obj.eps)
+        #     L1_tidle = [zeros(ComplexF64, dim, dim) for i = 1:para_num]
+        #     L2_tidle = [[zeros(ComplexF64, dim, dim) for i = 1:para_num] for j = 1:para_num]
+
+        #     for para_i = 1:para_num
+        #         for mi = 1:dim
+        #             p = (tr(ρt_T * obj.M[mi]) |> real)
+        #             dp = (tr(∂ρt_T[para_i] * obj.M[mi]) |> real)
+        #             if p > obj.eps
+        #                 L1_tidle[para_i] = L1_tidle[para_i] + dp * obj.M[mi] / p
+        #             end
+        #         end
+        #     end
+
+        #     for para_i = 1:para_num
+        #         dp_a = (tr(∂ρt_T[para_i] * obj.M[mi]) |> real)
+        #         for para_j = 1:para_num
+        #             dp_b = (tr(∂ρt_T[para_j] * obj.M[mi]) |> real)
+        #             for mi = 1:dim
+        #                 p = (tr(ρt_T * obj.M[mi]) |> real)
+        #                 if p > obj.eps
+        #                     L2_tidle[para_i][para_j] =
+        #                         L2_tidle[para_i][para_j] + dp_a * dp_b * obj.M[mi] / p^2
+        #                 end
+        #             end
+        #         end
+        #     end
+        #     coeff1 = real(det(F))
+        #     coeff2 =
+        #         obj.W[1, 1] * F_T[2, 2] + obj.W[2, 2] * F_T[1, 1] - obj.W[1, 2] * F_T[2, 1] -
+        #         obj.W[2, 1] * F_T[1, 2]
+        #     cost_function =
+        #         (abs(det(F_T)) < obj.eps ? (1.0 / obj.eps) : real(tr(obj.W * inv(F_T))))
+        #     for cm = 1:ctrl_num
+        #         for tm = 1:(tnum-1)
+        #             δF_all = [[0.0 for i = 1:para_num] for j = 1:para_num]
+        #             ∂ρt_T_δV = δρt_δV[cm][tm] |> vec2mat
+        #             for pm = 1:para_num
+        #                 for pn = 1:para_num
+        #                     ∂xδρt_T_δV_a = ∂xδρt_δV[pm][cm][tm] |> vec2mat
+        #                     ∂xδρt_T_δV_b = ∂xδρt_δV[pn][cm][tm] |> vec2mat
+        #                     term1 = tr(∂xδρt_T_δV_a * L1_tidle[pn])
+        #                     term2 = tr(∂xδρt_T_δV_b * L1_tidle[pm])
+        #                     term3 = tr(∂ρt_T_δV * L2_tidle[pm][pn])
+        #                     δF_all[pm][pn] = ((term1 + term2 - term3) |> real)
+        #                 end
+        #             end
+        #             item1 =
+        #                 -coeff2 * (
+        #                     F_T[2, 2] * δF_all[1][1] + F_T[1, 1] * δF_all[2][2] -
+        #                     F_T[2, 1] * δF_all[1][2] - F_T[1, 2] * δF_all[2][1]
+        #                 ) / coeff1^2
+        #             item2 =
+        #                 (
+        #                     obj.W[1, 1] * δF_all[2][2] + obj.W[2, 2] * δF_all[1][1] -
+        #                     obj.W[1, 2] * δF_all[2][1] - obj.W[2, 1] * δF_all[1][2]
+        #                 ) / coeff1
+        #             δF = -(item1 + item2) * cost_function^2
+        #             pdata.ctrl[cm][tm] = pdata.ctrl[cm][tm] + alg.epsilon * δF
+        #         end
+        #     end
     else
         F_T = CFIM(ρt_T, ∂ρt_T, obj.M; eps = obj.eps)
         L1_tidle = [zeros(ComplexF64, dim, dim) for i = 1:para_num]