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‎src/algorithm/AD.jl

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@@ -1,4 +1,8 @@
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#### control optimization ####
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"""
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Control optimization with autoGRAPE.
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"""
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function update!(opt::ControlOpt, alg::AbstractautoGRAPE, obj, dynamics, output)
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(; max_episode) = alg
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ctrl_length = length(dynamics.data.ctrl[1])
@@ -43,6 +47,10 @@ function update_ctrl!(alg::autoGRAPE, obj, dynamics, δ)
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end
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#### state optimization ####
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"""
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State optimization with AD.
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"""
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function update!(opt::StateOpt, alg::AbstractAD, obj, dynamics, output)
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(; max_episode) = alg
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f_ini, f_comp = objective(obj, dynamics)
@@ -76,6 +84,10 @@ function update_state!(alg::AD, obj, dynamics, δ)
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end
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#### find the optimal linear combination of a given set of POVM ####
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"""
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Measurement optimization (method: linear combination) with AD.
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"""
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function update!(opt::Mopt_LinearComb, alg::AbstractAD, obj, dynamics, output)
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(; max_episode) = alg
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(; POVM_basis, M_num) = opt
@@ -123,6 +135,10 @@ function update_M!(opt::Mopt_LinearComb, alg::AD, obj, δ)
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end
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#### find the optimal rotated measurement of a given set of POVM ####
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"""
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Measurement optimization (method: rotation) with AD.
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"""
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function update!(opt::Mopt_Rotation, alg::AbstractAD, obj, dynamics, output)
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(; max_episode) = alg
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(; POVM_basis) = opt
@@ -180,6 +196,10 @@ function update_M!(opt::Mopt_Rotation, alg::AD, obj, δ)
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end
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#### state abd control optimization ####
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"""
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Comprehensive optimization on state and control with AD.
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"""
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function update!(opt::StateControlOpt, alg::AbstractAD, obj, dynamics, output)
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(; max_episode) = alg
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ctrl_length = length(dynamics.data.ctrl[1])

‎src/algorithm/DDPG.jl

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@@ -41,6 +41,10 @@ mutable struct ControlEnv <: AbstractEnv
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end
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#### control optimization ####
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"""
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Control optimization with DDPG.
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"""
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function update!(opt::ControlOpt, alg::DDPG, obj, dynamics, output)
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(; max_episode, layer_num, layer_dim, rng) = alg
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#### environment of DDPG ####
@@ -243,6 +247,10 @@ RLBase.reward(env::StateEnv) = env.reward
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RLBase.is_terminated(env::StateEnv) = env.done
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RLBase.state(env::StateEnv) = env.state
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"""
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State optimization with DDPG.
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"""
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function update!(Sopt::StateOpt, alg::DDPG, obj, dynamics::Lindblad, output)
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(; max_episode, layer_num, layer_dim, rng) = alg
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episode = 1

‎src/algorithm/DE.jl

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Original file line numberDiff line numberDiff line change
@@ -1,4 +1,8 @@
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#### control optimization ####
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"""
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Control optimization with DE.
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"""
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function update!(opt::ControlOpt, alg::DE, obj, dynamics, output)
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(; max_episode, p_num, ini_population, c, cr, rng) = alg
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if ismissing(ini_population)
@@ -77,6 +81,10 @@ function update!(opt::ControlOpt, alg::DE, obj, dynamics, output)
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end
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#### state optimization ####
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"""
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State optimization with DE.
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"""
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function update!(opt::StateOpt, alg::DE, obj, dynamics, output)
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(; max_episode, p_num, ini_population, c, cr, rng) = alg
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if ismissing(ini_population)
@@ -140,6 +148,10 @@ function update!(opt::StateOpt, alg::DE, obj, dynamics, output)
140148
end
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#### projective measurement optimization ####
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"""
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Measurement optimization (method: projection) with DE.
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"""
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function update!(opt::Mopt_Projection, alg::DE, obj, dynamics, output)
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(; max_episode, p_num, ini_population, c, cr, rng) = alg
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if ismissing(ini_population)
@@ -222,6 +234,10 @@ function update!(opt::Mopt_Projection, alg::DE, obj, dynamics, output)
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end
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#### find the optimal linear combination of a given set of POVM ####
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"""
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Measurement optimization (method: linear combination) with DE.
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"""
225241
function update!(opt::Mopt_LinearComb, alg::DE, obj, dynamics, output)
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(; max_episode, p_num, ini_population, c, cr, rng) = alg
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(; B, POVM_basis, M_num) = opt
@@ -305,6 +321,10 @@ function update!(opt::Mopt_LinearComb, alg::DE, obj, dynamics, output)
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end
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307323
#### find the optimal rotated measurement of a given set of POVM ####
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"""
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Measurement optimization (method: rotation) with DE.
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"""
308328
function update!(opt::Mopt_Rotation, alg::DE, obj, dynamics, output)
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(; max_episode, p_num, ini_population, c, cr, rng) = alg
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(; s, POVM_basis, Lambda) = opt
@@ -398,6 +418,10 @@ function update!(opt::Mopt_Rotation, alg::DE, obj, dynamics, output)
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end
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400420
#### state and control optimization ####
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"""
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Comprehensive optimization on state and control with DE.
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"""
401425
function update!(opt::StateControlOpt, alg::DE, obj, dynamics, output)
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(; max_episode, p_num, ini_population, c, cr, rng) = alg
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if ismissing(ini_population)
@@ -497,6 +521,10 @@ function update!(opt::StateControlOpt, alg::DE, obj, dynamics, output)
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end
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#### state and measurement optimization ####
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"""
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Comprehensive optimization on state and measurement with DE.
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"""
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function update!(opt::StateMeasurementOpt, alg::DE, obj, dynamics, output)
501529
(; max_episode, p_num, ini_population, c, cr, rng) = alg
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if ismissing(ini_population)
@@ -600,6 +628,10 @@ function update!(opt::StateMeasurementOpt, alg::DE, obj, dynamics, output)
600628
end
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#### control and measurement optimization ####
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"""
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Comprehensive optimization on control and measurement with DE.
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"""
603635
function update!(opt::ControlMeasurementOpt, alg::DE, obj, dynamics, output)
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(; max_episode, p_num, ini_population, c, cr, rng) = alg
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if ismissing(ini_population)
@@ -714,6 +746,10 @@ function update!(opt::ControlMeasurementOpt, alg::DE, obj, dynamics, output)
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end
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#### state, control and measurement optimization ####
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"""
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Comprehensive optimization on state, control and measurement with DE.
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"""
717753
function update!(opt::StateControlMeasurementOpt, alg::DE, obj, dynamics, output)
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(; max_episode, p_num, ini_population, c, cr, rng) = alg
719755
if ismissing(ini_population)

‎src/algorithm/GRAPE.jl

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@@ -1,3 +1,9 @@
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"""
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update!(opt::ControlOpt, alg::AbstractGRAPE, obj::QFIM_obj, dynamics, output)
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Control optimization with GRAPE, where the objective is QFIM.
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"""
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function update!(opt::ControlOpt, alg::AbstractGRAPE, obj::QFIM_obj, dynamics, output)
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(; max_episode) = alg
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ctrl_length = length(dynamics.data.ctrl[1])
@@ -22,6 +28,10 @@ function update!(opt::ControlOpt, alg::AbstractGRAPE, obj::QFIM_obj, dynamics, o
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set_io!(output, output.f_list[end])
2329
end
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"""
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Control optimization with GRAPE, where the objective is CFIM.
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"""
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function update!(opt::ControlOpt, alg::AbstractGRAPE, obj::CFIM_obj, dynamics, output)
2636
(; max_episode) = alg
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ctrl_length = length(dynamics.data.ctrl[1])

‎src/objective/AsymptoticBound/CramerRao.jl

Lines changed: 10 additions & 7 deletions
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@@ -6,7 +6,7 @@ const σ_z = [1.0 0.0im; 0.0 -1.0]
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############## logarrithmic derivative ###############
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@doc raw"""
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9-
SLD(ρ::Matrix{T}, dρ::Vector{Matrix{T}}; rep = "original", eps = GLOBAL_EPS) where {T<:Complex}
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SLD(ρ::Matrix{T}, dρ::Vector{Matrix{T}}; rep = "original", eps = GLOBAL_EPS) Complex}
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Calculate the symmetric logarrithmic derivatives (SLDs).The SLD operator $L_a$ is defined as``\partial_{a}\rho=\frac{1}{2}(\rho L_{a}+L_{a}\rho)``, where ρ is the parameterized density matrix.
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9595
@doc raw"""
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RLD(ρ::Matrix{T}, dρ::Vector{Matrix{T}}; rep = "original", eps = GLOBAL_EPS) where {T<:Complex}
97+
RLD(ρ::Matrix{T}, dρ::Vector{Matrix{T}}; rep = "original", eps = GLOBAL_EPS) where {T<:Complex}
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Calculate the right logarrithmic derivatives (RLDs). The RLD operator is defined as ``\partial_{a}\rho=\rho \mathcal{R}_a``, where ρ is the parameterized density matrix.
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157157
@doc raw"""
158158
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LLD(ρ::Matrix{T}, dρ::Vector{Matrix{T}}; rep = "original", eps = GLOBAL_EPS) where {T<:Complex}
159+
LLD(ρ::Matrix{T}, dρ::Vector{Matrix{T}}; rep = "original", eps = GLOBAL_EPS) where {T<:Complex}
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Calculate the left logarrithmic derivatives (LLDs). The LLD operator is defined as ``\partial_{a}\rho=\mathcal{R}_a^{\dagger}\rho``, where ρ is the parameterized density matrix.
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- `ρ`: Density matrix.
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- `dρ`: Derivatives of the density matrix with respect to the unknown parameters to be estimated. For example, drho[1] is the derivative vector with respect to the first parameter.
164164
- `rep`: Representation of the LLD operator. Options can be:
165-
- "original" (default) -- The RLD matrix will be written in terms of the same basis as the input density matrix (ρ).
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- "eigen" -- The RLD matrix will be written in terms of the eigenbasis of the input ρ.
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- "original" (default) -- The RLD matrix will be written in terms of the same basis as the input density matrix (ρ).
166+
- "eigen" -- The RLD matrix will be written in terms of the eigenbasis of the input ρ.
167167
- `eps`: Machine epsilon
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"""
@@ -563,11 +563,14 @@ Calculate the SLD based quantum Fisher information matrix (QFIM) with gaussian s
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- `R̄` : First-order moment.
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- `dR̄`: Derivatives of the first-order moment with respect to the unknown parameters to be estimated. For example, dR[1] is the derivative vector on the first parameter.
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- `dR̄`: Derivatives of the first-order moment with respect to the unknown parameters to be
567+
estimated. For example, dR[1] is the derivative vector on the first
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parameter.
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- `D`: Second-order moment.
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- `dD`: Derivatives of the second-order moment with respect to the unknown parameters to be estimated.
572+
- `dD`: Derivatives of the second-order moment with respect to the unknown parameters to be
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estimated.
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- `eps`: Machine epsilon
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