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Profile Newton Propagation
# Include this file in a devrepl of
module TransmonModel
using LinearAlgebra
using SparseArrays
using QuantumControl.Shapes: flattop
const GHz = 2π
const MHz = 0.001GHz
const ns = 1.0
const μs = 1000ns
⊗ = kron
const 𝕚 = 1im
const N = 6 # levels per transmon
function hamiltonian(;
Ωre,
Ωim,
N=N, # levels per transmon
ω₁=4.380GHz,
ω₂=4.614GHz,
ωd=4.498GHz,
α₁=-210MHz,
α₂=-215MHz,
J=-3MHz,
λ=1.03,
use_sparse=:auto
)
𝟙 = SparseMatrixCSC{ComplexF64,Int64}(sparse(I, N, N))
b̂₁ = spdiagm(1 => complex.(sqrt.(collect(1:N-1)))) ⊗ 𝟙
b̂₂ = 𝟙 ⊗ spdiagm(1 => complex.(sqrt.(collect(1:N-1))))
b̂₁⁺ = sparse(b̂₁')
b̂₂⁺ = sparse(b̂₂')
n̂₁ = sparse(b̂₁' * b̂₁)
n̂₂ = sparse(b̂₂' * b̂₂)
n̂₁² = sparse(n̂₁ * n̂₁)
n̂₂² = sparse(n̂₂ * n̂₂)
b̂₁⁺_b̂₂ = sparse(b̂₁' * b̂₂)
b̂₁_b̂₂⁺ = sparse(b̂₁ * b̂₂')
ω̃₁ = ω₁ - ωd
ω̃₂ = ω₂ - ωd
Ĥ₀ = sparse(
(ω̃₁ - α₁ / 2) * n̂₁ +
(α₁ / 2) * n̂₁² +
(ω̃₂ - α₂ / 2) * n̂₂ +
(α₂ / 2) * n̂₂² +
J * (b̂₁⁺_b̂₂ + b̂₁_b̂₂⁺)
)
Ĥ₁re = (1 / 2) * (b̂₁ + b̂₁⁺ + λ * b̂₂ + λ * b̂₂⁺)
Ĥ₁im = (𝕚 / 2) * (b̂₁⁺ - b̂₁ + λ * b̂₂⁺ - λ * b̂₂)
if ((N < 5) && (use_sparse ≢ true)) || use_sparse ≡ false
H = (Array(Ĥ₀), (Array(Ĥ₁re), Ωre), (Array(Ĥ₁im), Ωim))
else
H = (Ĥ₀, (Ĥ₁re, Ωre), (Ĥ₁im, Ωim))
end
return H
end
function guess_pulses(; T=400ns, E₀=35MHz, dt=0.1ns, t_rise=15ns)
tlist = collect(range(0, T, step=dt))
Ωre = t -> E₀ * flattop(t, T=T, t_rise=t_rise)
Ωim = t -> 0.0
return tlist, Ωre, Ωim
end
function ket(i::Int64; N=N)
Ψ = zeros(ComplexF64, N)
Ψ[i+1] = 1
return Ψ
end
function ket(indices::Int64...; N=N)
Ψ = ket(indices[1]; N=N)
for i in indices[2:end]
Ψ = Ψ ⊗ ket(i; N=N)
end
return Ψ
end
function ket(label::AbstractString; N=N)
indices = [parse(Int64, digit) for digit in label]
return ket(indices...; N=N)
end
end # TransmonModel
module TransmonBenchmarks
using InteractiveUtils
using Profile
using ..TransmonModel: hamiltonian, ket, guess_pulses
using QuantumPropagators: _propagate
using QuantumControlBase:
objective_genfunc, initobjpropwrk, MinimalObjective, _propagate_objective
using BenchmarkTools
using PProf
suite = BenchmarkGroup(["method"])
N = 6
tlist, Ωre, Ωim = guess_pulses()
H = hamiltonian(; Ωre, Ωim, N=6, use_sparse=false)
Ψ₀ = ket("00"; N=6)
obj = MinimalObjective(initial_state=Ψ₀, generator=H)
genfunc = objective_genfunc(obj, tlist)
wrk_cheby = initobjpropwrk(obj, tlist, Val(:cheby))
wrk_newton = initobjpropwrk(obj, tlist, Val(:newton))
function run_benchmark()
_propagate(Ψ₀, genfunc, tlist, wrk_newton)
Profile.clear()
@pprof _propagate(Ψ₀, genfunc, tlist, wrk_newton)
end
end # TransmonBenchmarks
TransmonBenchmarks.run_benchmark()
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