Format .jl files

Author: LiuJPhys

docs/make.jl

@@ -5,23 +5,18 @@ using DocumenterCitations
 
 bib = CitationBibliography("src/refs.bib")
 
-makedocs(bib;
-    root    = ".",
-    source  = "src",
-    build   = "build",
-    clean   = true,
+makedocs(
+    bib;
+    root = ".",
+    source = "src",
+    build = "build",
+    clean = true,
     doctest = true,
     modules = [QuanEstimation],
     authors = "Hauiming Yu <[email protected]> and contributors",
     repo = "https://github.com/QuanEstimation/QuanEstimation.jl/blob/{commit}{path}#{line}",
     sitename = "QuanEstimation.jl",
-    pages = [
-        "API" => [
-            "api/GeneralAPI.md",
-            "api/BaseAPI.md",
-            "api/NVMagnetometerAPI.md"
-        ]
-    ]
+    pages = ["API" => ["api/GeneralAPI.md", "api/BaseAPI.md", "api/NVMagnetometerAPI.md"]],
 )
 
 #deploydocs(; repo = "github.com/QuanEstimation/QuanEstimation.jl")

examples/Bayesian_CramerRao_bounds.jl

@@ -3,56 +3,56 @@ using Trapz
 
 # free Hamiltonian
 function H0_func(x)
-    return 0.5*B*omega0*(sx*cos(x)+sz*sin(x))
+    return 0.5 * B * omega0 * (sx * cos(x) + sz * sin(x))
 end
 # derivative of the free Hamiltonian on x
 function dH_func(x)
-    return [0.5*B*omega0*(-sx*sin(x)+sz*cos(x))]
+    return [0.5 * B * omega0 * (-sx * sin(x) + sz * cos(x))]
 end
 # prior distribution
 function p_func(x, mu, eta)
-    return exp(-(x-mu)^2/(2*eta^2))/(eta*sqrt(2*pi))
+    return exp(-(x - mu)^2 / (2 * eta^2)) / (eta * sqrt(2 * pi))
 end
 function dp_func(x, mu, eta)
-    return -(x-mu)*exp(-(x-mu)^2/(2*eta^2))/(eta^3*sqrt(2*pi))
+    return -(x - mu) * exp(-(x - mu)^2 / (2 * eta^2)) / (eta^3 * sqrt(2 * pi))
 end
 
-B, omega0 = 0.5*pi, 1.0
-sx = [0. 1.; 1. 0.0im]
-sy = [0. -im; im 0.]
-sz = [1. 0.0im; 0. -1.]
+B, omega0 = 0.5 * pi, 1.0
+sx = [0.0 1.0; 1.0 0.0im]
+sy = [0.0 -im; im 0.0]
+sz = [1.0 0.0im; 0.0 -1.0]
 # initial state
-rho0 = 0.5*ones(2, 2)
+rho0 = 0.5 * ones(2, 2)
 # prior distribution
-x = range(-0.5*pi, stop=0.5*pi, length=100) |>Vector
+x = range(-0.5 * pi, stop = 0.5 * pi, length = 100) |> Vector
 mu, eta = 0.0, 0.2
 p_tp = [p_func(x[i], mu, eta) for i in eachindex(x)]
 dp_tp = [dp_func(x[i], mu, eta) for i in eachindex(x)]
 # normalization of the distribution
 c = trapz(x, p_tp)
-p = p_tp/c
-dp = dp_tp/c
+p = p_tp / c
+dp = dp_tp / c
 # time length for the evolution
-tspan = range(0., stop=1., length=1000)
+tspan = range(0.0, stop = 1.0, length = 1000)
 # dynamics
 rho = Vector{Matrix{ComplexF64}}(undef, length(x))
 drho = Vector{Vector{Matrix{ComplexF64}}}(undef, length(x))
-for i in eachindex(x) 
+for i in eachindex(x)
     H0_tp = H0_func(x[i])
     dH_tp = dH_func(x[i])
     rho_tp, drho_tp = QuanEstimation.expm(tspan, rho0, H0_tp, dH_tp)
     rho[i], drho[i] = rho_tp[end], drho_tp[end]
 end
 
 # Classical Bayesian bounds
-f_BCRB1 = QuanEstimation.BCRB([x], p, [], rho, drho, btype=1)
-f_BCRB2 = QuanEstimation.BCRB([x], p, [], rho, drho, btype=2)
-f_BCRB3 = QuanEstimation.BCRB([x], p, dp, rho, drho, btype=3)
+f_BCRB1 = QuanEstimation.BCRB([x], p, [], rho, drho, btype = 1)
+f_BCRB2 = QuanEstimation.BCRB([x], p, [], rho, drho, btype = 2)
+f_BCRB3 = QuanEstimation.BCRB([x], p, dp, rho, drho, btype = 3)
 f_VTB = QuanEstimation.VTB([x], p, dp, rho, drho)
 
 # Quantum Bayesian bounds
-f_BQCRB1 = QuanEstimation.BQCRB([x], p, [], rho, drho, btype=1)
-f_BQCRB2 = QuanEstimation.BQCRB([x], p, [], rho, drho, btype=2)
-f_BQCRB3 = QuanEstimation.BQCRB([x], p, dp, rho, drho, btype=3)
+f_BQCRB1 = QuanEstimation.BQCRB([x], p, [], rho, drho, btype = 1)
+f_BQCRB2 = QuanEstimation.BQCRB([x], p, [], rho, drho, btype = 2)
+f_BQCRB3 = QuanEstimation.BQCRB([x], p, dp, rho, drho, btype = 3)
 f_QVTB = QuanEstimation.QVTB([x], p, dp, rho, drho)
 f_QZZB = QuanEstimation.QZZB([x], p, rho)

examples/Bayesian_estimation.jl

@@ -4,31 +4,31 @@ using StatsBase
 
 # free Hamiltonian
 function H0_func(x)
-    return 0.5*B*omega0*(sx*cos(x)+sz*sin(x))
+    return 0.5 * B * omega0 * (sx * cos(x) + sz * sin(x))
 end
 # derivative of the free Hamiltonian on x
 function dH_func(x)
-    return [0.5*B*omega0*(-sx*sin(x)+sz*cos(x))]
+    return [0.5 * B * omega0 * (-sx * sin(x) + sz * cos(x))]
 end
 
-B, omega0 = pi/2.0, 1.0
-sx = [0. 1.; 1. 0.0im]
-sy = [0. -im; im 0.]
-sz = [1. 0.0im; 0. -1.]
+B, omega0 = pi / 2.0, 1.0
+sx = [0.0 1.0; 1.0 0.0im]
+sy = [0.0 -im; im 0.0]
+sz = [1.0 0.0im; 0.0 -1.0]
 # initial state
-rho0 = 0.5*ones(2, 2)
+rho0 = 0.5 * ones(2, 2)
 # measurement 
-M1 = 0.5*[1.0+0.0im  1.; 1.  1.]
-M2 = 0.5*[1.0+0.0im -1.; -1.  1.]
+M1 = 0.5 * [1.0+0.0im 1.0; 1.0 1.0]
+M2 = 0.5 * [1.0+0.0im -1.0; -1.0 1.0]
 M = [M1, M2]
 # prior distribution
-x = range(0., stop=0.5*pi, length=100) |>Vector
-p = (1.0/(x[end]-x[1]))*ones(length(x))
+x = range(0.0, stop = 0.5 * pi, length = 100) |> Vector
+p = (1.0 / (x[end] - x[1])) * ones(length(x))
 # time length for the evolution
-tspan = range(0., stop=1., length=1000)
+tspan = range(0.0, stop = 1.0, length = 1000)
 # dynamics
 rho = Vector{Matrix{ComplexF64}}(undef, length(x))
-for i in eachindex(x) 
+for i in eachindex(x)
     H0_tp = H0_func(x[i])
     dH_tp = dH_func(x[i])
     rho_tp, drho_tp = QuanEstimation.expm(tspan, rho0, H0_tp, dH_tp)
@@ -37,15 +37,15 @@ end
 
 # Generation of the experimental results
 Random.seed!(1234)
-y = [0 for i in 1:500]
-res_rand = sample(1:length(y), 125, replace=false)
+y = [0 for i = 1:500]
+res_rand = sample(1:length(y), 125, replace = false)
 for i in eachindex(res_rand)
     y[res_rand[i]] = 1
 end
 
 #===============Maximum a posteriori estimation===============#
-pout, xout = QuanEstimation.Bayes([x], p, rho, y; M=M, estimator="MAP",
-                                  savefile=false)
+pout, xout =
+    QuanEstimation.Bayes([x], p, rho, y; M = M, estimator = "MAP", savefile = false)
 
 #===============Maximum likelihood estimation===============#
-Lout, xout = QuanEstimation.MLE([x], rho, y, M=M; savefile=false)
+Lout, xout = QuanEstimation.MLE([x], rho, y, M = M; savefile = false)

examples/CMopt_NV.jl

@@ -6,50 +6,54 @@ using LinearAlgebra
 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.]
+sx = [0.0 1.0; 1.0 0.0]
+sy = [0.0 -im; im 0.0]
+sz = [1.0 0.0; 0.0 -1.0]
+s1 = [0.0 1.0 0.0; 1.0 0.0 1.0; 0.0 1.0 0.0] / sqrt(2)
+s2 = [0.0 -im 0.0; im 0.0 -im; 0.0 im 0.0] / sqrt(2)
+s3 = [1.0 0.0 0.0; 0.0 0.0 0.0; 0.0 0.0 -1.0]
 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)])
+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
+dH = gS * S + gI * Is
 # control Hamiltonians 
 Hc = [S1, S2, S3]
 # dissipation
-decay = [[S3, 2pi/cons]]
+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]
+POVM_basis = [QuanEstimation.basis(dim, i) * QuanEstimation.basis(dim, i)' for i = 1:dim]
 # time length for the evolution
-tspan = range(0., 2., length=4000)
+tspan = range(0.0, 2.0, length = 4000)
 # control and measurement optimization
-opt = QuanEstimation.CMopt(ctrl_bound=[-0.2,0.2], seed=1234)
+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)
+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)   
+dynamics =
+    QuanEstimation.Lindblad(opt, tspan, rho0, H0, dH, Hc, decay = decay, dyn_method = :Expm)
 # objective function: CFI
-obj = QuanEstimation.CFIM_obj() 
+obj = QuanEstimation.CFIM_obj()
 # run the comprehensive optimization problem
-QuanEstimation.run(opt, alg, obj, dynamics; savefile=false)
+QuanEstimation.run(opt, alg, obj, dynamics; savefile = false)
 
 ##-------------algorithm: PSO---------------------##
 # alg = QuanEstimation.PSO(p_num=10, max_episode=[1000,100], c0=1.0, 

examples/CMopt_qubit.jl

@@ -1,40 +1,40 @@
 using QuanEstimation
 
 # initial state
-rho0 = 0.5*ones(2, 2)
+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
+sx = [0.0 1.0; 1.0 0.0im]
+sy = [0.0 -im; im 0.0]
+sz = [1.0 0.0im; 0.0 -1.0]
+H0 = 0.5 * omega * sz
 # derivative of the free Hamiltonian on omega
-dH = [0.5*sz]
+dH = [0.5 * sz]
 # control Hamiltonians 
 Hc = [sx, sy, sz]
 # dissipation
-sp = [0. 1.; 0. 0.0im]
-sm = [0. 0.; 1. 0.0im]
+sp = [0.0 1.0; 0.0 0.0im]
+sm = [0.0 0.0; 1.0 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.]
+M1 = 0.5 * [1.0+0.0im 1.0; 1.0 1.0]
+M2 = 0.5 * [1.0+0.0im -1.0; -1.0 1.0]
 M = [M1, M2]
 # time length for the evolution
-tspan = range(0., 10., length=2500)
+tspan = range(0.0, 10.0, length = 2500)
 # control and measurement optimization
-opt = QuanEstimation.CMopt(ctrl_bound=[-2.0,2.0], seed=1234)
+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)
+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)   
+dynamics =
+    QuanEstimation.Lindblad(opt, tspan, rho0, H0, dH, Hc, decay = decay, dyn_method = :Expm)
 # objective function: CFI
-obj = QuanEstimation.CFIM_obj() 
+obj = QuanEstimation.CFIM_obj()
 # run the comprehensive optimization problem
-QuanEstimation.run(opt, alg, obj, dynamics; savefile=false)
+QuanEstimation.run(opt, alg, obj, dynamics; savefile = false)
 
 ##-------------algorithm: PSO---------------------##
 # alg = QuanEstimation.PSO(p_num=10, max_episode=[1000,100], c0=1.0, 

examples/CramerRao_bounds.jl

@@ -1,30 +1,30 @@
 using QuanEstimation
 
 # initial state
-rho0 = 0.5*ones(2, 2)
+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
+sx = [0.0 1.0; 1.0 0.0im]
+sy = [0.0 -im; im 0.0]
+sz = [1.0 0.0im; 0.0 -1.0]
+H0 = 0.5 * omega * sz
 # derivative of the free Hamiltonian on omega
-dH = [0.5*sz]
+dH = [0.5 * sz]
 # dissipation
-sp = [0. 1.; 0. 0.0im]
-sm = [0. 0.; 1. 0.0im]
+sp = [0.0 1.0; 0.0 0.0im]
+sm = [0.0 0.0; 1.0 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.]
+M1 = 0.5 * [1.0+0.0im 1.0; 1.0 1.0]
+M2 = 0.5 * [1.0+0.0im -1.0; -1.0 1.0]
 M = [M1, M2]
 # time length for the evolution
-tspan = range(0., 50., length=2000)
+tspan = range(0.0, 50.0, length = 2000)
 # dynamics
-rho, drho = QuanEstimation.expm(tspan, rho0, H0, dH, decay=decay)
+rho, drho = QuanEstimation.expm(tspan, rho0, H0, dH, decay = decay)
 # calculation of the CFI and QFI
 Im, F = Float64[], Float64[]
-for ti in 2:length(tspan)
+for ti = 2:length(tspan)
     # CFI
     I_tp = QuanEstimation.CFIM(rho[ti], drho[ti], M)
     append!(Im, I_tp)

examples/HCRB_NHB.jl

@@ -2,32 +2,32 @@ using QuanEstimation
 using LinearAlgebra
 
 # initial state
-psi0 = [1., 0., 0., 1.]/sqrt(2)
-rho0 = psi0*psi0'
+psi0 = [1.0, 0.0, 0.0, 1.0] / sqrt(2)
+rho0 = psi0 * psi0'
 # free Hamiltonian
 omega1, omega2, g = 1.0, 1.0, 0.1
-sx = [0. 1.; 1. 0.0im]
-sy = [0. -im; im 0.]
-sz = [1. 0.0im; 0. -1.]
-H0 = omega1*kron(sz, I(2)) + omega2*kron(I(2), sz) + g*kron(sx, sx)
+sx = [0.0 1.0; 1.0 0.0im]
+sy = [0.0 -im; im 0.0]
+sz = [1.0 0.0im; 0.0 -1.0]
+H0 = omega1 * kron(sz, I(2)) + omega2 * kron(I(2), sz) + g * kron(sx, sx)
 # derivatives of the free Hamiltonian with respect to omega2 and g
 dH = [kron(I(2), sz), kron(sx, sx)]
 # dissipation
 decay = [[kron(sz, I(2)), 0.05], [kron(I(2), sz), 0.05]]
 # measurement
-m1 = [1., 0., 0., 0.]
-M1 = 0.85*m1*m1'
-M2 = 0.1*ones(4, 4)
-M = [M1, M2, I(4)-M1-M2]
+m1 = [1.0, 0.0, 0.0, 0.0]
+M1 = 0.85 * m1 * m1'
+M2 = 0.1 * ones(4, 4)
+M = [M1, M2, I(4) - M1 - M2]
 # weight matrix
 W = one(zeros(2, 2))
 # time length for the evolution
-tspan = range(0., 5., length=200)
+tspan = range(0.0, 5.0, length = 200)
 # dynamics
 rho, drho = QuanEstimation.expm(tspan, rho0, H0, dH, decay)
 # calculation of the CFIM, QFIM and HCRB
 f_HCRB, f_NHB = [], []
-for ti in 2:length(tspan)
+for ti = 2:length(tspan)
     # HCRB
     f_tp1 = QuanEstimation.HCRB(rho[ti], drho[ti], W)
     append!(f_HCRB, f_tp1)