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Merged
hmyuuu merged 1 commit into from
Aug 8, 2022
Merged

Adaptive -> Adapt #51

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2 changes: 1 addition & 1 deletion examples/adaptive.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -46,5 +46,5 @@ pout, xout = QuanEstimation.Bayes([x], p, rho, y, M=M, savefile=false)
H, dH = QuanEstimation.BayesInput([x], H0_func, dH_func;
channel="dynamics")
# adaptive measurement
QuanEstimation.Adaptive([x], pout, rho0, tspan, H, dH; M=M,
QuanEstimation.Adapt([x], pout, rho0, tspan, H, dH; M=M,
max_episode=100)
8 changes: 4 additions & 4 deletions src/Common/AdaptiveScheme.jl
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@@ -1,6 +1,6 @@
@doc raw"""

Adaptive(x::AbstractVector, p, rho0::AbstractMatrix, tspan, H, dH; method="FOP", savefile=false, max_episode::Int=1000, eps::Float64=1e-8, Hc=missing, ctrl=missing, decay=missing, M=missing, W=missing)
Adapt(x::AbstractVector, p, rho0::AbstractMatrix, tspan, H, dH; method="FOP", savefile=false, max_episode::Int=1000, eps::Float64=1e-8, Hc=missing, ctrl=missing, decay=missing, M=missing, W=missing)

In QuanEstimation, the Hamiltonian of the adaptive system should be written as
``H(\textbf{x}+\textbf{u})`` with ``\textbf{x}`` the unknown parameters and ``\textbf{u}``
Expand All @@ -22,7 +22,7 @@ Hamiltonian work at the optimal point ``\textbf{x}_{\mathrm{opt}}``.
- `M`: A set of positive operator-valued measure (POVM). The default measurement is a set of rank-one symmetric informationally complete POVM (SIC-POVM).
- `W`: Whether or not to save all the posterior distributions.
"""
function Adaptive(x::AbstractVector, p, rho0::AbstractMatrix, tspan, H, dH; method="FOP", savefile=false, max_episode::Int=1000, eps::Float64=1e-8,
function Adapt(x::AbstractVector, p, rho0::AbstractMatrix, tspan, H, dH; method="FOP", savefile=false, max_episode::Int=1000, eps::Float64=1e-8,
Hc=missing, ctrl=missing, decay=missing, M=missing, W=missing)
dim = size(rho0)[1]
para_num = length(x)
Expand Down Expand Up @@ -165,7 +165,7 @@ end

@doc raw"""

Adaptive(x::AbstractVector, p, rho0::AbstractMatrix, K, dK; method="FOP", savefile=false, max_episode::Int=1000, eps::Float64=1e-8, M=missing, W=missing)
Adapt(x::AbstractVector, p, rho0::AbstractMatrix, K, dK; method="FOP", savefile=false, max_episode::Int=1000, eps::Float64=1e-8, M=missing, W=missing)

In QuanEstimation, the Hamiltonian of the adaptive system should be written as
``H(\textbf{x}+\textbf{u})`` with ``\textbf{x}`` the unknown parameters and ``\textbf{u}``
Expand All @@ -183,7 +183,7 @@ Hamiltonian work at the optimal point ``\textbf{x}_{\mathrm{opt}}``.
- `M`: A set of positive operator-valued measure (POVM). The default measurement is a set of rank-one symmetric informationally complete POVM (SIC-POVM).
- `W`: Whether or not to save all the posterior distributions.
"""
function Adaptive(x::AbstractVector, p, rho0::AbstractMatrix, K, dK; method="FOP", savefile=false, max_episode::Int=1000,
function Adapt(x::AbstractVector, p, rho0::AbstractMatrix, K, dK; method="FOP", savefile=false, max_episode::Int=1000,
eps::Float64=1e-8, M=missing, W=missing)
dim = size(rho0)[1]
para_num = length(x)
Expand Down