promote dual in master equations

Project.toml

@@ -7,6 +7,7 @@ Arpack = "7d9fca2a-8960-54d3-9f78-7d1dccf2cb97"
 DiffEqBase = "2b5f629d-d688-5b77-993f-72d75c75574e"
 DiffEqCallbacks = "459566f4-90b8-5000-8ac3-15dfb0a30def"
 FFTW = "7a1cc6ca-52ef-59f5-83cd-3a7055c09341"
+FiniteDiff = "6a86dc24-6348-571c-b903-95158fe2bd41"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 IterativeSolvers = "42fd0dbc-a981-5370-80f2-aaf504508153"
 KrylovKit = "0b1a1467-8014-51b9-945f-bf0ae24f4b77"

src/master.jl

@@ -14,6 +14,7 @@ function master_h(tspan, rho0::Operator, H::AbstractOperator, J;
     _check_const.(J)
     _check_const.(Jdagger)
     check_master(rho0, H, J, Jdagger, rates)
+    tspan, rho0 = _promote_time_and_state(rho0, H, J, tspan)
     tmp = copy(rho0)
     dmaster_(t, rho, drho) = dmaster_h!(drho, H, J, Jdagger, rates, rho, tmp)
     integrate_master(tspan, dmaster_, rho0, fout; kwargs...)
@@ -41,6 +42,7 @@ function master_nh(tspan, rho0::Operator, Hnh::AbstractOperator, J;
     _check_const.(J)
     _check_const.(Jdagger)
     check_master(rho0, Hnh, J, Jdagger, rates)
+    tspan, rho0 = _promote_time_and_state(rho0, Hnh, J, tspan)
     tmp = copy(rho0)
     dmaster_(t, rho, drho) = dmaster_nh!(drho, Hnh, Hnhdagger, J, Jdagger, rates, rho, tmp)
     integrate_master(tspan, dmaster_, rho0, fout; kwargs...)
@@ -86,6 +88,7 @@ function master(tspan, rho0::Operator, H::AbstractOperator, J;
     _check_const(H)
     _check_const.(J)
     _check_const.(Jdagger)
+    tspan, rho0 = _promote_time_and_state(rho0, H, J, tspan)
     isreducible = check_master(rho0, H, J, Jdagger, rates)
     if !isreducible
         tmp = copy(rho0)
@@ -124,6 +127,7 @@ function master(tspan, rho0::Operator, L::SuperOperator; fout=nothing, kwargs...
     b = GenericBasis(dim)
     rho_ = Ket(b,reshape(rho0.data, dim))
     L_ = Operator(b,b,L.data)
+    tspan, rho_ = _promote_time_and_state(rho_, L_, tspan)
     dmaster_(t,rho,drho) = dmaster_liouville!(drho,L_,rho)
 
     # Rewrite into density matrix when saving

src/schroedinger.jl

@@ -122,31 +122,3 @@ function check_schroedinger(psi::Bra, H)
     check_multiplicable(psi, H)
     check_samebases(H)
 end
-
-
-function _promote_time_and_state(u0, H::AbstractOperator, tspan)
-    Ts = eltype(H)
-    Tt = real(Ts)
-    p = Vector{Tt}(undef,0)
-    u0_promote = DiffEqBase.promote_u0(u0, p, tspan[1])
-    tspan_promote = DiffEqBase.promote_tspan(u0_promote, p, tspan, nothing, Dict{Symbol, Any}())
-    return tspan_promote, u0_promote
-end
-_promote_time_and_state(u0, f, tspan) = _promote_time_and_state(u0, f(first(tspan), u0), tspan)
-
-@inline function DiffEqBase.promote_u0(u0::T, p, t0) where {T<:Union{Bra,Ket}}
-    u0data_promote = DiffEqBase.promote_u0(u0.data, p, t0)
-    if u0data_promote !== u0.data
-        u0_promote = T(u0.basis, u0data_promote)
-        return u0_promote
-    end
-    return u0
-end
-@inline function DiffEqBase.promote_u0(u0::Operator, p, t0)
-    u0data_promote = DiffEqBase.promote_u0(u0.data, p, t0)
-    if u0data_promote !== u0.data
-        u0_promote = Operator(u0.basis_l, u0.basis_r, u0data_promote)
-        return u0_promote
-    end
-    return u0
-end

src/timeevolution_base.jl

@@ -121,3 +121,47 @@ macro skiptimechecks(ex)
 end
 
 Base.@pure pure_inference(fout,T) = Core.Compiler.return_type(fout, T)
+
+function _promote_time_and_state(u0, H::AbstractOperator, tspan)
+    Ts = eltype(H)
+    Tt = real(Ts)
+    p = Vector{Tt}(undef,0)
+    u0_promote = DiffEqBase.promote_u0(u0, p, tspan[1])
+    tspan_promote = DiffEqBase.promote_tspan(u0_promote.data, p, tspan, nothing, Dict{Symbol, Any}())
+    return tspan_promote, u0_promote
+end
+function _promote_time_and_state(u0, H::AbstractOperator, J, tspan)
+    Ts = DiffEqBase.promote_dual(eltype(H), DiffEqBase.anyeltypedual(J))
+    Tt = real(Ts)
+    p = Vector{Tt}(undef,0)
+    u0_promote = DiffEqBase.promote_u0(u0, p, tspan[1])
+    tspan_promote = DiffEqBase.promote_tspan(u0_promote.data, p, tspan, nothing, Dict{Symbol, Any}())
+    return tspan_promote, u0_promote
+end
+
+_promote_time_and_state(u0, f, tspan) = _promote_time_and_state(u0, f(first(tspan)..., u0), tspan)
+
+@inline function DiffEqBase.promote_u0(u0::Ket, p, t0)
+    u0data_promote = DiffEqBase.promote_u0(u0.data, p, t0)
+    if u0data_promote !== u0.data
+        u0_promote = Ket(u0.basis, u0data_promote)
+        return u0_promote
+    end
+    return u0
+end
+@inline function DiffEqBase.promote_u0(u0::Bra, p, t0)
+    u0data_promote = DiffEqBase.promote_u0(u0.data, p, t0)
+    if u0data_promote !== u0.data
+        u0_promote = Bra(u0.basis, u0data_promote)
+        return u0_promote
+    end
+    return u0
+end
+@inline function DiffEqBase.promote_u0(u0::Operator, p, t0)
+    u0data_promote = DiffEqBase.promote_u0(u0.data, p, t0)
+    if u0data_promote !== u0.data
+        u0_promote = Operator(u0.basis_l, u0.basis_r, u0data_promote)
+        return u0_promote
+    end
+    return u0
+end

test/test_ForwardDiff.jl

@@ -1,6 +1,7 @@
 using Test
 using OrdinaryDiffEq, QuantumOptics
 import ForwardDiff
+import FiniteDiff
 
 # for some caese ForwardDiff.jl returns NaN due to issue with DiffEq.jl. see https://github.com/SciML/DiffEqBase.jl/issues/861
 # Here we test;
@@ -73,3 +74,35 @@ Ftdop(1.0)
 @test ForwardDiff.derivative(Ftdop, 1.0) isa Any
 
 end # testset
+
+
+@testset "ForwardDiff with `master`" begin
+
+b = SpinBasis(1//2)
+psi0 = spindown(b)
+rho0 = dm(psi0)
+params = [10.0, -3.0]
+
+# test to see if parameter propagates through Hamiltonian
+H(p) = p[1]*sigmax(b) + p[2]*sigmam(b)  # Hamiltonian
+function cost1(p) #
+    tf, psif = timeevolution.master((0.0, pi), rho0, H(p), [sigmax(b)])
+    return 1 - norm(psif)
+end
+
+forwarddiff1 = ForwardDiff.gradient(cost1, params)
+finitediff1 = FiniteDiff.finite_difference_gradient(cost1, params)
+@test isapprox(forwarddiff1, finitediff1; atol=1e-5)
+
+# test to see if parameter propagates through Jump operator
+J(p) = p[1]*sigmax(b) + p[2]*sigmam(b)  # jump operator
+function cost2(p)
+    tf, psif = timeevolution.master((0.0, pi), rho0, sigmax(b), [J(p)])
+    return 1 - norm(psif)
+end
+
+forwarddiff2 = ForwardDiff.gradient(cost2, params)
+finitediff2 = FiniteDiff.finite_difference_gradient(cost2, params)
+@test isapprox(forwarddiff2, finitediff2; atol=1e-5)
+
+end