Time Evolution Solutions

Solution

QuantumToolbox utilizes the powerful DifferentialEquation.jl to simulate different kinds of quantum system dynamics. Thus, we will first look at the data structure used for returning the solution (sol) from DifferentialEquation.jl. The solution stores all the crucial data needed for analyzing and plotting the results of a simulation. A generic structure TimeEvolutionSol contains the following properties for storing simulation data:

Fields (Attributes)	Description
sol.times	The time list of the evolution.
sol.states	The list of result states.
sol.expect	The expectation values corresponding to each time point in sol.times.
sol.alg	The algorithm which is used during the solving process.
sol.abstol	The absolute tolerance which is used during the solving process.
sol.reltol	The relative tolerance which is used during the solving process.
sol.retcode (or sol.converged)	The returned status from the solver.

Accessing data in solutions

To understand how to access the data in solution, we will use an example as a guide, although we do not worry about the simulation details at this stage. The Schrödinger equation solver (sesolve) used in this example returns TimeEvolutionSol:

H = 0.5 * sigmay()
ψ0 = basis(2, 0)
e_ops = [
    proj(basis(2, 0)),
    proj(basis(2, 1)),
    basis(2, 0) * basis(2, 1)'
]
tlist = LinRange(0, 10, 100)
sol = sesolve(H, ψ0, tlist, e_ops = e_ops, progress_bar = Val(false))

To see what is contained inside the solution, we can use the print function:

print(sol)

Solution of time evolution
(return code: Success)
--------------------------
num_states = 1
num_expect = 3
ODE alg.: OrdinaryDiffEqTsit5.Tsit5{typeof(OrdinaryDiffEqCore.trivial_limiter!), typeof(OrdinaryDiffEqCore.trivial_limiter!), Static.False}(OrdinaryDiffEqCore.trivial_limiter!, OrdinaryDiffEqCore.trivial_limiter!, static(false))
abstol = 1.0e-8
reltol = 1.0e-6

It tells us the number of expectation values are computed and the number of states are stored. Now we have all the information needed to analyze the simulation results. To access the data for the three expectation values, one can do:

expt1 = real(sol.expect[1,:])
expt2 = real(sol.expect[2,:])
expt3 = real(sol.expect[3,:])

Recall that Julia uses Fortran-style indexing that begins with one (i.e., [1,:] represents the 1-st observable, where : represents all values corresponding to tlist).

Together with the array of times at which these expectation values are calculated:

times = sol.times

we can plot the resulting expectation values:

# plot by CairoMakie.jl
fig = Figure(size = (500, 350))
ax = Axis(fig[1, 1], xlabel = L"t")
lines!(ax, times, expt1, label = L"\langle 0 | \rho(t) | 0 \rangle")
lines!(ax, times, expt2, label = L"\langle 1 | \rho(t) | 1 \rangle")
lines!(ax, times, expt3, label = L"\langle 0 | \rho(t) | 1 \rangle")

ylims!(ax, (-0.5, 1.0))
axislegend(ax, position = :lb)

fig

State vectors, or density matrices, are accessed in a similar manner:

sol.states

1-element Vector{QuantumObject{Ket, Dimensions{1, Tuple{Space}}, Vector{ComplexF64}}}:
 
Quantum Object:   type=Ket()   dims=[2]   size=(2,)
2-element Vector{ComplexF64}:
  0.2836622138778657 + 0.0im
 -0.9589242324580376 + 0.0im

Here, the solution contains only one (final) state. Because the states will be saved depend on the keyword argument saveat in kwargs. If e_ops is empty, the default value of saveat=tlist (saving the states corresponding to tlist), otherwise, saveat=[tlist[end]] (only save the final state). One can also specify e_ops and saveat separately.

Some other solvers can have other output.

Multiple trajectories solution

This part is still under construction, please read the docstrings for the following functions first:

• TimeEvolutionMCSol

• TimeEvolutionStochasticSol