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dynamical_map.jl
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dynamical_map.jl
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### A Pluto.jl notebook ###
# v0.17.0
using Markdown
using InteractiveUtils
# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
quote
local el = $(esc(element))
global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : missing
el
end
end
# ╔═╡ 104b6690-212b-11ec-3a2b-25c420f62534
begin
using Plots
using LinearAlgebra
using Kronecker
using PlutoUI
using SparseArrays
#https://juliapackages.com/p/kronecker
md"""## Packages"""
end
# ╔═╡ 21712488-f636-4861-913c-012693cb524d
md"""# Order of tensor product basis (TPB)"""
# ╔═╡ 89b58dd0-c70c-4566-b991-2d739990f2ff
md"""
Note, that throughout the thesis a colexicographical ordering of the tensor product is preferred:
$\ket{a_1 b_1}, \ket{a_2 b_1}, \ket{a_1 b_2}, \ket{a_2 b_2}$. In this case the tensor product is realized via the conventional Kronecker product but using flipped arguments:
$A \otimes B \rightarrow \boldsymbol{B} \otimes \boldsymbol{A}$.
Here you can choose the ordering of the tensor product basis of the tensor product ``\mathcal{H}_{\mathcal{A}} \otimes \mathcal{H}_{\mathcal{B}}``:
👉 $(@bind order Select(["colex" => "Colexicographical order", "lex" => "Lexicographical order"]))
"""
# ╔═╡ 23c019dd-53d2-461e-a6ba-f1b5da2f6710
if order == "colex"
md"""
``\begin{pmatrix}{\color{magenta}a}&{\color{red}C}\\ \boldsymbol{b} & D \end{pmatrix} \overset{\text{c}} {\otimes}\begin{pmatrix} 1 & \large 3 \\ {\color{blue}2} & \scriptsize {\color{green}4}\end{pmatrix} = \begin{pmatrix}{\color{magenta}a}1 & {\color{red}C}1 & {\color{magenta}a} \large 3 & {\color{red}C} \large 3 \\\boldsymbol{b}1 & D1 & \boldsymbol{b} \large 3 & D \large 3 \\ {\color{magenta}a}{\color{blue}2} & {\color{red}C}{\color{blue}2} & {\color{magenta}a}\scriptsize {\color{green}4} & {\color{red}C}\scriptsize {\color{green}4} \\ \boldsymbol{b}{\color{blue}2} & D{\color{blue}2} & \boldsymbol{b}\scriptsize {\color{green}4} & D\scriptsize {\color{green}4} \end{pmatrix}, \quad A \overset{\text{c}}{\otimes} B:= \textbf{B} \otimes \textbf{A}``
"""
else
md"""
``\begin{pmatrix}{\color{magenta}a}&{\color{red}C}\\ \boldsymbol{b} & D \end{pmatrix} {\otimes}\begin{pmatrix} 1 & \large 3 \\ {\color{blue}2} & \scriptsize {\color{green}4}\end{pmatrix} = \begin{pmatrix}{\color{magenta}a}1 & {\color{magenta}a} \large 3 & {\color{red}C}1 & {\color{red}C} \large 3 \\{\color{magenta}a}{\color{blue}2} & {\color{magenta}a}\scriptsize {\color{green}4} & {\color{red}C}{\color{blue}2} & {\color{red}C}\scriptsize {\color{green}4} \\ \boldsymbol{b}1 & \boldsymbol{b} \large 3 & D1 & D \large 3 \\ \boldsymbol{b}{\color{blue}2} & \boldsymbol{b}\scriptsize {\color{green}4} & D{\color{blue}2} & D\scriptsize {\color{green}4} \end{pmatrix}``
"""
end
# ╔═╡ f5992b42-ff99-479a-b093-70a9bfd85655
@show order
# ╔═╡ 0a95d032-8576-492f-bee1-ef67303bcb3a
begin
if order == "colex"
⊗(A,B) = kron(B,A)
else
⊗(A,B) = kron(A,B)
end
end
# ╔═╡ 7458474c-3c4d-4a99-9640-4bfd2e6465ea
@bind 🍎 Scrubbable(1:10)
# ╔═╡ edf8bfba-ca6b-4adf-9a7d-e618ca511a80
@bind time_r Clock()
# ╔═╡ d7f3dc01-1201-41de-8a63-c3e2bc020cf6
σ₄ = 5
# ╔═╡ 2e2854f6-8cab-4252-a580-7abc71efd2a0
rand(2,2) ⊗ I(2)
# ╔═╡ a646342f-3177-4579-8625-cb7d7ea27a49
🍎 + σ₄ + time_r
# ╔═╡ 792748ec-199c-41d8-a801-6cd97881ee72
🍎*4
# ╔═╡ 85bca31d-c3be-46b6-b330-b80648472378
md"""
# System Hamiltonian of bipartite system
"""
# ╔═╡ ebd5efea-94b6-419e-8ff1-9dcf624ddaba
md"""
The system Hamiltonian for a bipartite system $\mathcal{H}_{\mathcal{A}} \otimes \mathcal{H}_{\mathcal{B}}$ is given by
$H = \sum_{i = \{\mathcal{A}, \mathcal{B}\}} \varepsilon_i \hat{n}_i + \sum_{i=1}^3 \alpha_i (\mathbb{1}_{\mathcal{A}} \otimes {\sigma}_i) + \sum_{i=1}^3 \beta_i ({\sigma}_i \otimes \mathbb{1}_{\mathcal{B}}) + \sum_{ij=1}^3 \gamma_{ij} ({\sigma}_i \otimes {\sigma}_j)$
with Pauli matrices
$\sigma_1 = \sigma_x = \begin{pmatrix} 0 & 1 \\ 1 & 0 \end{pmatrix}$,
$\sigma_2 = \sigma_y = \begin{pmatrix} 0 & -i \\ i & 0 \end{pmatrix}$,
$\sigma_3 = \sigma_z = \begin{pmatrix} 1 & 0 \\ 0 & -1\end{pmatrix}$
"""
# ╔═╡ 913d405a-e525-49d6-9021-227543b37953
md"""
# Initial state ``ρ(t_0)``
"""
# ╔═╡ a36e5515-e7f9-4c34-a48e-52a004d80f23
md"""
Switch from different initial states 👉 $(@bind initial_state Select(["thesis" => "Example in 📖", "example" => "Another example 📍","slider"=> "Choose your own initial state 📐"]))
"""
# ╔═╡ 7479e663-e533-442c-8324-c60f4849c470
md"""
# Dynamical map for different initial states
"""
# ╔═╡ 0872a46c-4f4b-49fa-8152-9193b71f332b
md"""
## Initial product state
"""
# ╔═╡ 5e1377b7-c784-4e42-8190-ffbf48f0ecc1
md"""
## Initial classical-quantum correlated state
"""
# ╔═╡ 822ea5ab-4a3a-45ee-903c-ff9bad9d5f16
md"""
## Example of non--positive dynamical map
"""
# ╔═╡ 4cdab1a4-1784-4430-ae4f-50a4459d83a4
md"""
# Example of a non-accessible map
The transposition superoperator
"""
# ╔═╡ a558b847-3f09-473c-9ec5-70badcda7589
md"""
# Appendix: Functions and Initializations
"""
# ╔═╡ 8407fc8c-95b4-4536-bbda-b3f4945518b0
TableOfContents()
# ╔═╡ 7f43573f-010f-455a-82c7-f2f509cce3d3
begin
time_slider = @bind time Slider(0:0.1:20, default = 1, show_value = true)
md""" ## Interactive widgets
Definition of Sliders"""
end
# ╔═╡ e8c989d6-9ba6-40d1-ad36-d768172d5f5e
time_slider
# ╔═╡ 002504f4-e74a-4e8c-80fa-f4c5f2e90df0
time_slider
# ╔═╡ d28b8274-7435-484a-bde7-61443d7e4ae2
begin
scrub_1 = @bind s1 Scrubbable(-0.5:0.01:0.5, format=".03")
scrub_2 = @bind s2 Scrubbable(-0.5:0.01:0.5, format=".03")
scrub_3 = @bind s3 Scrubbable(-0.5:0.01:0.5, format=".03")
scrub_4 = @bind s4 Scrubbable(-0.5:0.01:0.5, format=".03")
scrub_5 = @bind s5 Scrubbable(-0.5:0.01:0.5, format=".03")
scrub_6 = @bind s6 Scrubbable(-0.5:0.01:0.5, format=".03")
scrub_7 = @bind s7 Scrubbable(-0.5:0.01:0.5, format=".03", default = 0.25)
scrub_8 = @bind s8 Scrubbable(-0.5:0.01:0.5, format=".03")
scrub_9 = @bind s9 Scrubbable(-0.50:0.01:0.50, format=".03")
scrub_a1 = @bind a1 Scrubbable(-0.50:0.01:0.50, format=".03", default = -0.5)
scrub_a2 = @bind a2 Scrubbable(-0.50:0.01:0.50, format=".03")
scrub_a3 = @bind a3 Scrubbable(-0.50:0.01:0.50, format=".03")
scrub_b1 = @bind b1 Scrubbable(-0.50:0.01:0.50, format=".03", default = -0.5)
scrub_b2 = @bind b2 Scrubbable(-0.50:0.01:0.50, format=".03")
scrub_b3 = @bind b3 Scrubbable(-0.50:0.01:0.50, format=".03")
scrub_i1 = @bind i1 Scrubbable(-0.50:0.01:0.50, format=".03", default = -0.5)
scrub_i2 = @bind i2 Scrubbable(-0.50:0.01:0.50, format=".03")
scrub_i3 = @bind i3 Scrubbable(-0.50:0.01:0.50, format=".03")
md" Definition of Scrubbables"
end
# ╔═╡ fc12024b-2785-4f89-a457-95a2deb5045f
begin
if initial_state == "slider"
md"""
You can choose the parameters of the initial state:
$\rho(t_0) =\frac{1}{4}\mathbb{1}_{\mathcal{A}} \otimes \mathbb{1}_{\mathcal{B}}+ \sum_{i=1}^3 \alpha_i (\mathbb{1}_{\mathcal{A}} \otimes {\sigma}_i) + \sum_{i=1}^3 \beta_i ({\sigma}_i \otimes \mathbb{1}_{\mathcal{B}}) + \sum_{ij=1}^3 \gamma_{ij} ({\sigma}_i \otimes {\sigma}_j)$
``\alpha_1`` 👉 $(scrub_a1), ``\alpha_2`` 👉 $(scrub_a2), ``\alpha_3`` 👉 $(scrub_a3)
``\beta_1`` 👉 $(scrub_b1), ``\beta_2`` 👉 $(scrub_b2), ``\beta_3`` 👉 $(scrub_b3)
``\gamma_{11}`` 👉 $(scrub_1), ``\gamma_{12}`` 👉 $(scrub_2), ``\gamma_{13}`` 👉 $(scrub_3)
``\gamma_{21}`` 👉 $(scrub_4), ``\gamma_{22}`` 👉 $(scrub_5), ``\gamma_{23}`` 👉 $(scrub_6)
``\gamma_{31}`` 👉 $(scrub_7), ``\gamma_{32}`` 👉 $(scrub_8), ``\gamma_{33}`` 👉 $(scrub_9)
"""
end
end
# ╔═╡ 1e4adc4d-52cf-41df-ad62-cab016f55397
[scrub_i1, scrub_i2, scrub_i3]
# ╔═╡ 0c09a3b6-920d-4732-af67-86b36bd88292
scrub_a1, scrub_a2, scrub_a3
# ╔═╡ dc95c17d-de0f-4b72-b57e-0dab473a69cd
[scrub_1,scrub_2,scrub_3,scrub_4, scrub_5, scrub_6, scrub_7, scrub_8, scrub_9]
# ╔═╡ 75ebae9f-1c69-4d92-9cdf-a0f4b0971576
begin
σ = [[0 1; 1 0], [0 -im; im 0], [1 0; 0 -1]]
function qu_bit_system(epsilon = 1,
alpha = [0,0,0],
beta= [0,0,0],
gamma = zeros(3,3))
return H = epsilon * kron(I(2),I(2)) +
sum([(I(2) ⊗ σ[i]) * alpha[i] for i = 1:3]) +
sum([(σ[i] ⊗ I(2)) * beta[i] for i=1:3]) +
sum([gamma[i,j] * (σ[i] ⊗ σ[j]) for i=1:3, j=1:3])
end
function density_pauli(α)
return α[1]/2 * I(2) + sum([α[i+1] * σ[i] for i = 1:3])
end
function density_matrix(n, neg = 0, real_valued = false)
#trace 1 remains preserved!
dia = rand(n)
if neg > 0
dia[rand(1:n, neg)] .*= -1
end
dia = dia/sum(dia)
if real_valued
temp = rand(n,n)
else
temp = rand(n,n) + rand(n,n) * im
end
ew, ev = eigen(Hermitian(temp'*temp))
d_mat = Hermitian(ev* Diagonal(dia) * ev')
return d_mat
end
function unitary(n)
A = (rand(n,n) .-0.5) + im*(rand(n,n) .-0.5)
return eigvecs(A'+A)
end
function unity(i,n)
E = spzeros(Bool,n,n)
E[i] = true
return E
end
function partial_trace_matrix(dim_1, dim_2, traced_system, order="lex")
# traces out the system indicated by traced_system
# for C = kron(A,B), tracing over the first system (traced_system = 1);
# yields: reshape(p1*C[:], 4,4)/tr(A) == B
#
# when colex ordered:
# flip traced_system
if order == "colex"
traced_system = 3 - traced_system
end
if traced_system == 1
T = [kron(sparse(I, dim_1,dim_1), unity(i,dim_2))[:] for i = 1:dim_2^2]
col = reduce(vcat,[findnz(T[i])[1] for i = 1:dim_2^2])
row = kron(1:dim_2^2, ones(Int64,dim_1))
return sparse(row, col, trues(dim_2^2*dim_1))
else
T = [kron(unity(i, dim_1), sparse(I, dim_2, dim_2))[:] for i = 1:dim_1^2]
col = reduce(vcat, [findnz(T[i])[1] for i = 1:dim_1^2])
row = kron(1:dim_1^2, ones(Int64,dim_2))
return sparse(row, col, trues(dim_1^2*dim_2))
#return Tr_B = [reshape(kron(reshape(unity(i,dim_1), dim_1, dim_1), I(dim_2)), 1, dim_2^2 * dim_1^2)[1,j] for i = 1:dim_1^2, j =1:dim_1^2*dim_2^2]
end
end
function choi(A, dim_1)
mm, nn = size(A)
n = mm ÷ dim_1
return reshape(permutedims(reshape(A,n,dim_1,n,dim_1),(1,3,2,4)), mm,mm)
end
function choi_rect(A,dim_1, dim_2, dim_3, dim_4)
return reshape(permutedims(reshape(A, dim_1, dim_2, dim_3, dim_4), (1,3,2,4)), dim_1*dim_3, dim_2*dim_4)
end
function choi_index(i, n)
return (i.-1) .% n[1] .+ 1 .+
n[1] .* ((i.-1) .% prod(n[1:3]) .÷ prod(n[1:2]) .+
n[3] .* ((i.-1) .% prod(n[1:2]) .÷ n[1] .+
n[2] .* ((i.-1) .÷ prod(n[1:3]))))
end
function ptr(ρ, dim_1, traced_system = 1, order="lex")
# get partial trace superoperator
N,M = size(ρ)
dim_2 = N÷dim_1
PT = partial_trace_matrix(dim_1, dim_2, traced_system, order)
new_dim = traced_system == 1 ? dim_2 : dim_1
# apply superoperator to vectorized density matrix and reshape to new dimensions
σ = reshape(PT * ρ[:], new_dim, new_dim)
return σ
end
function factorize(A, dim_1, dim_2, order="lex")
σ = ptr(A, dim_1, 2, order)
ρ = ptr(A, dim_1, 1, order)
A_tensor_prod = (σ ⊗ ρ)
rho_c = A - A_tensor_prod
return A_tensor_prod, rho_c
end
function normed_vector(k)
v = (rand(k) .- 0.5) + im*(rand(k) .- 0.5)
return v/norm(v)
end
function prob_dist(k)
p = rand(k)
return p/sum(p)
end
function PPT(rho,dim_1)
dim_2 = size(rho,1)÷dim_1
if dim_1 *dim_2 > 6
error("positive partial trace does not work for too large dimensions")
end
return eigvals(reshape(permutedims(reshape(rho,dim_1,dim_2,dim_1,dim_2), [1,4,3,2]),dim_1*dim_2, dim_1*dim_2))
end
function sep_state(k=1,n=2)
VVV = sum([(normed_vector(n) ⊗ normed_vector(n)) for _=1:k] .* (prob_dist(k)))
return VVV / norm(VVV)
end
#test for k-block positivity
# strategy: generate random matrices in 2x2 and test whether Choi matrix maps to positive operators, if true for all, then positive.
# alternatively: check k-positivity: k as optional parameter. Create states with Schmidt rank k
#Schmidt rank:
function pos_test(choi, k = 1, n = 2)
runs = 10000
test = zeros(runs)
vector = zeros(ComplexF64, n^2)
for i = 1:runs
#sum([density_matrix(2) for _=1:k] * p_rand/sum(p_rand))
VVN = sep_state(k,n)
test[i] = real(VVN'* choi *VVN)
if real(test[i]) < 0
vector = VVN
end
end
return sum(test .> 0) / runs
end
md"""
# Functions
"""
end
# ╔═╡ c8e02ee8-2c21-4a37-9875-c4a463e0b462
begin
ϵ = [1,2]
α = [0,0,0]
β = [0,0,0]
γ = [1 0 0; 0 0 0; 0 0 0]
H = qu_bit_system(0, α, β, γ) + I(2) ⊗ Diagonal([0,ϵ[2]]) +
Diagonal([0,ϵ[1]]) ⊗ I(2)
end
# ╔═╡ a5e38ca3-8184-4160-a6b1-4c1783c6e185
begin
if initial_state == "thesis"
rho_0 = qu_bit_system(1/4, [0.0, 0.00, +0.0],
[0.00, -0.0, -0.0], [0 0 0; 0 0 0; 1/4 0 0])
elseif initial_state == "example"
rho_0 = qu_bit_system(1/4, [0.05, 0.00, +0.0],
[0.00, 0.05, -0.0], [0 0 0.05; 0.05 0 0; 0.05 0 0])
else
rho_0 = qu_bit_system(1/4, [a1, a2, a3],
[b1, b2, b3], [s1 s2 s3; s4 s5 s6;s7 s8 s9])
end
# alternative
Dim = 2
RHO_b = [density_matrix(Dim) for _=1:Dim]
RHO_s = density_matrix(Dim)
evals, Pro = eigen(RHO_s)
prob = prob_dist(Dim)
#rho_0 = sum([kron(Pro[:,i] * Pro[:,i]', RHO_b[i]) for i = 1:Dim] .*prob)
rho_t_0, rho_c = factorize(rho_0,2,2, order)
end
# ╔═╡ 4f87569b-777e-4e97-ac80-8b7b76115d2a
rho_0
# ╔═╡ b595349b-b572-4cc8-92b1-07631cc8fad7
eigvals(rho_0) # eigenvalues of the initial state
# ╔═╡ 8cd0438c-c480-4b73-93af-79e3f3a5dfa9
eigvals(rho_0)
# ╔═╡ 596a9128-2fff-47e8-8b6f-e9fd8686a279
[ptr(rho_0,2,2,order), ptr(rho_0,2,1,order)] #σ(t_0) and ρ_B(t_0)
# ╔═╡ c578581e-467e-482c-9cce-2ad55d32267f
PPT(rho_0,2) #initial state is separable, but is it classical quantum correlated?
# ╔═╡ 8c779399-7db0-421e-b1b2-659d28fc8c41
begin
#using the product state rho_t_0
rho_B = ptr(rho_t_0,2,1, order) # the bath denstiy matrix
sigma_0 = ptr(rho_t_0,2,2, order) # the system density matrix
compare = sigma_0 ⊗ rho_B - rho_t_0 #check if this is really a product state
U = exp(-im*H*time)
#NOTE: the choi matrix notation needs a transpose the match the lexicographical order
if order == "colex"
Λ = choi(choi(U,2) * kron(rho_B, I(2)) * choi(U,2)',2)#
else
Λ = transpose(choi(transpose(choi(U,2)) * kron(I(2), rho_B) * transpose(choi(U,2))',2)) # this is a real Kronecker product
end
Λ_c = ptr(U * rho_c * U', 2, 2, order)[:] * collect(I(2)[:])'
test = density_matrix(2)
dyn_1 = reshape(Λ * test[:], 2,2)
dyn_2 = ptr(U * (test ⊗ rho_B) * U',2,2, order)
dyn_1 - dyn_2
end
# ╔═╡ 9330d1cf-e0e1-41c1-a876-5fae11633ce8
Λ_c + Λ
# ╔═╡ 04583ab3-2364-4c32-be8e-d3e65ca5321e
round.(Λ + Λ_c, digits=2)
# ╔═╡ 5d450f8d-c8c4-4a58-b2a6-97bfe109f91f
begin
#check
norm(ptr(U*rho_0*U',2,2, order)- reshape((Λ+Λ_c) * ptr(rho_0,2,2, order)[:],2,2))
end
# ╔═╡ 4fb4c614-ed74-411e-91db-641c331c75fa
eigvals(choi(Λ + Λ_c,2))
# map is not completely positive
# ╔═╡ 36df576e-5111-4d66-9170-2eb9573cea32
pos_test(choi(Λ + Λ_c,2),1,2)
# dynamical map is not positive
# ╔═╡ 2cab05f7-bc59-4d26-a535-495b5ab104c0
begin
sig_n = 1/2 * I(2) + i1 * σ[1] + i2 * σ[2] + i3*σ[3]
#pure state: a1 = -0.5
eigvals(sig_n)
#sig_n is not compatible:
eigvals((sig_n ⊗ rho_B) + rho_c)
end
# ╔═╡ faf456bc-9145-4d87-ad4d-6ecc8c78bd41
eigvals(reshape((Λ + Λ_c)* sig_n[:],2,2))
# yields negative result
# ╔═╡ e63d17ba-767c-4136-8dc3-3da609f8dda5
reshape((Λ + Λ_c)* sig_n[:],2,2)
# ╔═╡ 5695cdc2-0da6-4ef1-9aa9-d553f9b194e4
begin
K_p = [1/2*(I(2) + σ[3]), 1/2*(I(2) - σ[3]), 1/sqrt(2)*σ[1]]
K_m = 1/sqrt(2)*[im * σ[2]]
Λ_t = sum([kron(conj.(K_p[i]), K_p[i]) for i = 1:3]) - kron(conj.(K_m[1]), K_m[1])
choi(Λ_t,2)
C_t = sum([K_p[i][:] * K_p[i][:]' for i = 1:3]) - K_m[1][:] * K_m[1][:]'
C_t - kron(I(2), density_pauli([0,a1,a2,a3]))
end
# ╔═╡ 3140729e-d516-434d-ba32-3ff9705ee83b
begin
keep_working(text=md"The answer is not quite right.", title="Keep working on it!") = Markdown.MD(Markdown.Admonition("danger", title, [text]));
almost(text, title="Almost there!") = Markdown.MD(Markdown.Admonition("warning", title, [text]));
hint(text, title ="Hint") = Markdown.MD(Markdown.Admonition("hint", title, [text]));
correct(text=md"Great! You got the right answer! Let's move on to the next section.", title="Got it!") = Markdown.MD(Markdown.Admonition("correct", title, [text]));
md" Definition of Boxes"
end
# ╔═╡ 5afc42f7-3253-438d-96a8-d665a1b9a1cd
if any(eigvals(rho_0) .< 0)
keep_working(md"Tune the parameters such, that the initial state becomes positive: ``\rho(t_0)> 0``.
The current eigenvalues are:
$(string(round.(eigvals(rho_0), digits = 2)))" ,"Initial state not valid!")
end
# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
Kronecker = "2c470bb0-bcc8-11e8-3dad-c9649493f05e"
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8"
SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
[compat]
Kronecker = "~0.5.0"
Plots = "~1.22.3"
PlutoUI = "~0.7.12"
"""
# ╔═╡ 00000000-0000-0000-0000-000000000002
PLUTO_MANIFEST_TOML_CONTENTS = """
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[[IteratorInterfaceExtensions]]
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[[JLLWrappers]]
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[[JSON]]
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[[JpegTurbo_jll]]
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[[Kronecker]]
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[[LAME_jll]]
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[[LZO_jll]]
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[[LaTeXStrings]]
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[[LibCURL]]
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[[LibCURL_jll]]
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[[LibGit2]]
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[[LibSSH2_jll]]
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[[Libdl]]
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[[Libffi_jll]]
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[[Libgcrypt_jll]]
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[[Libglvnd_jll]]
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[[Libgpg_error_jll]]
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[[Libiconv_jll]]
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[[Libmount_jll]]
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[[Measures]]
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[[MozillaCACerts_jll]]
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[[Ogg_jll]]
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[[OpenSSL_jll]]
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