Merge pull request #120 from QuanEstimation/test-adpt-online

update tests for adaptmzi online

lib/QuanEstimationBase/ext/QuanEstimationBasePyExt.jl

@@ -209,7 +209,7 @@ function QuanEstimationBase.ode_py(
     ctrl_num = length(Hc)
     ctrl_interval = ((length(tspan) - 1) / length(ctrl[1])) |> Int
     ctrl =
-        [repeat(ctrl[i], 1, ctrl_interval) |> transpose |> vec |> Array for i = 1:ctrl_num]
+        [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]
@@ -252,7 +252,7 @@ function QuanEstimationBase.ode_py(
     ctrl_num = length(Hc)
     ctrl_interval = ((length(tspan) - 1) / length(ctrl[1])) |> Int
     ctrl =
-        [repeat(ctrl[i], 1, ctrl_interval) |> transpose |> vec |> Array for i = 1:ctrl_num]
+        [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

@@ -7,7 +7,7 @@ function optimize!(opt::ControlOpt, alg::DE, obj, scheme, output)
     ini_population = ini_population[1]
     ctrl_length = get_ctrl_length(scheme)
     ctrl_num = get_ctrl_num(scheme)
-    populations = repeat(scheme, p_num)
+    populations = repeat_copy(scheme, p_num)
 
     # initialization
     initial_ctrl!(opt, ini_population, populations, p_num, opt.rng)
@@ -84,7 +84,7 @@ function optimize!(opt::StateOpt, alg::DE, obj, scheme, output)
     end
     ini_population = ini_population[1]
     dim = get_dim(scheme)
-    populations = repeat(scheme, p_num)
+    populations = repeat_copy(scheme, p_num)
     # initialization  
     initial_state!(ini_population, populations, p_num, opt.rng)
 
@@ -426,7 +426,7 @@ function optimize!(opt::StateControlOpt, alg::DE, obj, scheme, output)
     ctrl_length = get_ctrl_length(scheme)
     ctrl_num = get_ctrl_num(scheme)
     dim = get_dim(scheme)
-    populations = repeat(scheme, p_num)
+    populations = repeat_copy(scheme, p_num)
 
     # initialization 
     initial_state!(psi0, populations, p_num, opt.rng)
@@ -533,7 +533,7 @@ function optimize!(opt::StateMeasurementOpt, alg::DE, obj, scheme, output)
     psi0, measurement0 = ini_population
     dim = get_dim(scheme)
     M_num = length(opt.M)
-    populations = repeat(scheme, p_num)
+    populations = repeat_copy(scheme, p_num)
 
     # initialization 
     initial_state!(psi0, populations, p_num, opt.rng)
@@ -645,7 +645,7 @@ function optimize!(opt::ControlMeasurementOpt, alg::DE, obj, scheme, output)
     ctrl_num = get_ctrl_num(scheme)
 
     M_num = length(opt.M)
-    populations = repeat(scheme, p_num)
+    populations = repeat_copy(scheme, p_num)
 
     # initialization 
     initial_ctrl!(opt, ctrl0, populations, p_num, opt.rng)
@@ -763,7 +763,7 @@ function optimize!(opt::StateControlMeasurementOpt, alg::DE, obj, scheme, output
     ctrl_length = get_ctrl_length(scheme)
     ctrl_num = get_ctrl_num(scheme)
     M_num = length(opt.M)
-    populations = repeat(scheme, p_num)
+    populations = repeat_copy(scheme, p_num)
 
     # initialization 
     initial_state!(psi0, populations, p_num, opt.rng)

lib/QuanEstimationBase/src/Algorithm/NM.jl

@@ -4,7 +4,7 @@ function optimize!(opt::StateOpt, alg::NM, obj, scheme, output)
         ini_state = [opt.psi]
     end
     dim = get_dim(scheme)
-    nelder_mead = repeat(scheme, p_num)
+    nelder_mead = repeat_copy(scheme, p_num)
 
     # initialize 
     if length(ini_state) > p_num

lib/QuanEstimationBase/src/Algorithm/PSO.jl

@@ -10,7 +10,7 @@ function optimize!(opt::ControlOpt, alg::PSO, obj, scheme, output)
     ini_particle = ini_particle[1]
     ctrl_length = get_ctrl_length(scheme)
     ctrl_num = get_ctrl_num(scheme)
-    particles = repeat(scheme, p_num)
+    particles = repeat_copy(scheme, p_num)
 
     if typeof(max_episode) == Int
         max_episode = [max_episode, max_episode]
@@ -94,7 +94,7 @@ function optimize!(opt::ControlOpt, alg::PSO, obj, scheme, output)
         end
         if ei % max_episode[2] == 0
             pdata.ctrl = [gbest[k, :] for k = 1:ctrl_num]
-            particles = repeat(scheme, p_num)
+            particles = repeat_copy(scheme, p_num)
         end
 
         set_f!(output, fit_out)
@@ -116,7 +116,7 @@ function optimize!(opt::StateOpt, alg::PSO, obj, scheme, output)
     end
     ini_particle = ini_particle[1]
     dim = get_dim(scheme)
-    particles = repeat(scheme, p_num)
+    particles = repeat_copy(scheme, p_num)
 
     if typeof(max_episode) == Int
         max_episode = [max_episode, max_episode]
@@ -178,7 +178,7 @@ function optimize!(opt::StateOpt, alg::PSO, obj, scheme, output)
         end
         if ei % max_episode[2] == 0
             sdata = [gbest[i] for i = 1:dim]
-            particles = repeat(scheme, p_num)
+            particles = repeat_copy(scheme, p_num)
         end
         set_f!(output, fit_out)
         set_buffer!(output, [gbest[i] for i = 1:dim])
@@ -407,7 +407,7 @@ function optimize!(opt::Mopt_Rotation, alg::PSO, obj, scheme, output)
     #     append!(Lambda, [suN[i] for i in eachindex(suN)])
     # end
 
-    particles = repeat(s, p_num)
+    particles = repeat_copy(s, p_num)
 
     if typeof(max_episode) == Int
         max_episode = [max_episode, max_episode]
@@ -502,7 +502,7 @@ function optimize!(opt::StateControlOpt, alg::PSO, obj, scheme, output)
     dim = get_dim(scheme)
     ctrl_length = get_ctrl_length(scheme)
     ctrl_num = get_ctrl_num(scheme)
-    particles = repeat(scheme, p_num)
+    particles = repeat_copy(scheme, p_num)
 
     if typeof(max_episode) == Int
         max_episode = [max_episode, max_episode]
@@ -628,7 +628,7 @@ function optimize!(opt::StateMeasurementOpt, alg::PSO, obj, scheme, output)
     psi0, measurement0 = ini_particle
     dim = get_dim(scheme)
     M_num = length(opt.M)
-    particles = repeat(scheme, p_num)
+    particles = repeat_copy(scheme, p_num)
 
     if typeof(max_episode) == Int
         max_episode = [max_episode, max_episode]
@@ -750,7 +750,7 @@ function optimize!(opt::ControlMeasurementOpt, alg::PSO, obj, scheme, output)
     ctrl_num = get_ctrl_num(scheme)
     dim = get_dim(scheme)
     M_num = length(opt.M)
-    particles = repeat(scheme, p_num)
+    particles = repeat_copy(scheme, p_num)
 
     if typeof(max_episode) == Int
         max_episode = [max_episode, max_episode]
@@ -888,7 +888,7 @@ function optimize!(opt::StateControlMeasurementOpt, alg::PSO, obj, scheme, outpu
     ctrl_num = get_ctrl_num(scheme)
     dim = get_dim(scheme)
     M_num = length(opt.M)
-    particles = repeat(scheme, p_num)
+    particles = repeat_copy(scheme, p_num)
 
     if typeof(max_episode) == Int
         max_episode = [max_episode, max_episode]

lib/QuanEstimationBase/src/Common/Common.jl

@@ -1,4 +1,4 @@
-include("mintime.jl")
+# include("mintime.jl")
 include("BayesEstimation.jl")
 
 destroy(N) = diagm(1 => [sqrt(n) + 0.0im for n = 1:N-1])
@@ -17,18 +17,8 @@ function vec2mat(x)
     vec2mat.(x)
 end
 
-function vec2mat(x::Matrix)
-    throw(ErrorException("vec2mating a matrix of size $(size(x))"))
-end
-
-unzip(X) = map(x -> getfield.(X, x), fieldnames(eltype(X)))
-
-function Base.repeat(system, N)
-    [deepcopy(system) for i = 1:N]
-end
-
-function Base.repeat(system, M, N)
-    reshape(repeat(system, M * N), M, N)
+function repeat_copy(scheme, N)
+    [deepcopy(scheme) for _ = 1:N]
 end
 
 function filterZeros!(x::Matrix{T}) where {T<:Complex}

lib/QuanEstimationBase/src/Scheme/EstimationStrategy/AdaptiveEstimation/AdptiveMZI.jl

@@ -21,12 +21,12 @@ Online adaptive phase estimation in the MZI.
 - `target`: Setting the target function for calculating the tunable phase. Options are: "sharpness" and "MI".
 - `output`: Choose the output variables. Options are: "phi" and "dphi".
 """
-function online(apt::Adapt_MZI; target::Symbol = :sharpness, output::String = "phi", res=nothing)
+function online(apt::Adapt_MZI; target::String = "sharpness", output::String = "phi", res=nothing)
     (; x, p, rho0) = apt
-    adaptMZI_online(x, p, rho0, Symbol(output), target; res=res)
+    adaptMZI_online(x, p, rho0, Symbol(output),  Symbol(target); res=res)
 end
 
-function adaptMZI_online(x, p, rho0, output, target::Symbol; res=nothing)
+function adaptMZI_online(x, p, rho0, output, target; res=nothing)
     N = Int(sqrt(size(rho0, 1))) - 1
     a = destroy(N + 1) |> sparse
     exp_ix = [exp(1.0im * xi) for xi in x]
@@ -120,9 +120,6 @@ function adaptMZI_online(x, p, rho0, output, target::Symbol; res=nothing)
     end
 end
 
-adaptMZI_online(x, p, rho0, output::String, target::String) =
-    adaptMZI_online(x, p, rho0, Symbol(output), Symbol(target))
-
 function calculate_online{sharpness}(x, p, pyx, a_res, a, rho0, N, ei, phi_span, exp_ix)
 
     M_res = zeros(length(phi_span))
@@ -320,34 +317,6 @@ function DE_deltaphiOpt(
     return deltaphi[findmax(p_fit)[2]]
 end
 
-DE_deltaphiOpt(
-    x,
-    p,
-    rho0,
-    comb,
-    p_num,
-    ini_population,
-    c,
-    cr,
-    seed::Number,
-    max_episode,
-    target::String,
-    eps,
-) = DE_deltaphiOpt(
-    x,
-    p,
-    rho0,
-    comb,
-    p_num,
-    ini_population,
-    c,
-    cr,
-    MersenneTwister(seed),
-    max_episode,
-    Symbol(target),
-    eps,
-)
-
 function PSO_deltaphiOpt(
     x,
     p,

lib/QuanEstimationBase/src/Scheme/Parameterization/Lindblad/LindbladDynamics.jl

@@ -11,18 +11,6 @@ function dissipation(Γ::V, γ::Vector{R}, t::Int = 1) where {V<:AbstractVector,
     [γ[i] * liouville_dissip(Γ[i]) for i in eachindex(Γ)] |> sum
 end
 
-function dissipation(
-    Γ::V,
-    γ::Vector{Vector{R}},
-    t::Int = 1,
-) where {V<:AbstractVector,R<:Real}
-    [γ[i][t] * liouville_dissip(Γ[i]) for i in eachindex(Γ)] |> sum
-end
-
-function free_evolution(H0)
-    -1.0im * liouville_commu(H0)
-end
-
 function liouvillian(H::Matrix{T}, decay_opt::AbstractVector, γ, t = 1) where {T<:Complex}
     freepart = liouville_commu(H)
     dissp = norm(γ) + 1 ≈ 1 ? freepart |> zero : dissipation(decay_opt, γ, t)
@@ -33,13 +21,13 @@ function Htot(H0::T, Hc::V, ctrl) where {T<:Matrix{ComplexF64},V<:AbstractVector
     [H0 + sum([ctrl[i][t] * Hc[i] for i in eachindex(Hc)]) for t in eachindex(ctrl[1])]
 end
 
-function Htot(
-    H0::T,
-    Hc::V,
-    ctrl::Vector{R},
-) where {T<:AbstractArray,V<:AbstractVector,R<:Real}
-    H0 + ([ctrl[i] * Hc[i] for i in eachindex(ctrl)] |> sum)
-end
+# function Htot(
+#     H0::T,
+#     Hc::V,
+#     ctrl::Vector{R},
+# ) where {T<:AbstractArray,V<:AbstractVector,R<:Real}
+#     H0 + ([ctrl[i] * Hc[i] for i in eachindex(ctrl)] |> sum)
+# end
 
 
 # function Htot(H0::V1, Hc::V2, ctrl) where {V1<:AbstractVector,V2<:AbstractVector}

test/objective/test_cramer_rao_bound.jl

@@ -40,6 +40,7 @@ function test_cramer_rao_bound_multi_param()
     QFIM_LLD(rho[end], drho[end][1])
     QFIM_pure(rho0, [zero(rho0) for _ = 1:2])
     QFIM_pure(rho0, zero(rho0))
+    NHB(rho[end], drho[end], one(zeros(2, 2)))
 
     @test all([tr(pinv(i)) >= 0 for i in Im])
     @test all([tr(pinv(f)) >= 0 for f in F])

test/optimization/test_state_optimization.jl

@@ -73,9 +73,9 @@ function test_sopt_qfim(; savefile = false)
 end
 
 function test_sopt_cfi(; savefile = false)
-    (; tspan, psi, H0, dH, decay) = generate_LMG1_dynamics()
+    (; tspan, psi, H0, dH) = generate_LMG1_dynamics()
 
-    dynamics = Lindblad(H0, dH, tspan, decay; dyn_method = :Expm)
+    dynamics = Lindblad(H0, dH, tspan; dyn_method = :Expm)
     scheme = GeneralScheme(; probe = psi, param = dynamics)
 
     obj = CFIM_obj()

test/test_adaptive_estimation.jl

@@ -14,7 +14,10 @@ function test_adaptive_estimation_MZI()
     apt = Adapt_MZI(x, p, rho0)
 
     #================online strategy=========================#
-    online(apt; target=:sharpness, output="phi", res=zeros(2))
+    online(apt; target="sharpness", output="phi", res=zeros(2))
+    online(apt; target="MI", output="phi", res=zeros(2))
+    online(apt; target="sharpness", output="dphi", res=zeros(2))
+    online(apt; target="MI", output="dphi", res=zeros(2))
 
     #================offline strategy=========================#
     # algorithm: DE
@@ -23,7 +26,7 @@ function test_adaptive_estimation_MZI()
     offline(apt, alg, target = :MI, seed = 1234)
 
     # # algorithm: PSO
-    PSO(p_num=3, ini_particle=nothing, max_episode=[10,10], c0=1.0, c1=2.0, c2=2.0)
+    alg = PSO(p_num=3, ini_particle=nothing, max_episode=[10,10], c0=1.0, c1=2.0, c2=2.0)
     offline(apt, alg, target=:sharpness, seed=1234)