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TLS_State_to_State.jl
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1780 lines (1461 loc) · 58.1 KB
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### A Pluto.jl notebook ###
# v0.20.13
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)
#! format: off
return quote
local iv = try Base.loaded_modules[Base.PkgId(Base.UUID("6e696c72-6542-2067-7265-42206c756150"), "AbstractPlutoDingetjes")].Bonds.initial_value catch; b -> missing; end
local el = $(esc(element))
global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : iv(el)
el
end
#! format: on
end
# ╔═╡ 55f8b637-5938-4a3d-8d82-62e03507296b
begin
using LinearAlgebra
import QuantumControl
using PlutoUI
using Plots
import Pkg
Plots.default(
linewidth = 3,
size = (550, 300),
legend = :right,
foreground_color_legend = nothing,
background_color_legend = RGBA(1, 1, 1, 0.8)
)
TableOfContents()
end
# ╔═╡ 017e2537-5315-4431-a4fd-9d514efb8d7c
using QuantumControl: hamiltonian
# ╔═╡ ffeedd55-31f9-49c2-9ef4-de7e3d4db19e
using QuantumControl: Trajectory
# ╔═╡ 982d89b2-f541-40a7-9048-1a3107e2ea19
using QuantumControl: propagate_trajectory
# ╔═╡ 447018f0-b509-42e3-91cd-6bdad4d4f83f
using QuantumPropagators: ExpProp
# ╔═╡ 187c295d-c3c6-45da-89d6-c8bc520a7dab
using QuantumControl: ControlProblem
# ╔═╡ 11edb079-c331-4ae4-9c94-c18e0384e6f0
using QuantumControl.Functionals: J_T_sm
# ╔═╡ f8a135c1-d9a0-4377-a080-65790d9d48e2
using QuantumControl: @optimize_or_load
# ╔═╡ 74e4da66-8cb3-4f3a-bc52-bd99ddae170c
using GRAPE
# ╔═╡ d0facb2c-c898-4298-912e-b61a3f8be6a1
using Krotov
# ╔═╡ 4407caad-a77d-4b14-b924-24ea2273d36b
using QuantumControl.Controls: substitute
# ╔═╡ fe2480b6-153a-11f0-0c2d-35f102edea84
md"""
# State-To-State Transfer in a Two-Level-System
``
\newcommand{tr}[0]{\operatorname{tr}}
\newcommand{diag}[0]{\operatorname{diag}}
\newcommand{abs}[0]{\operatorname{abs}}
\newcommand{pop}[0]{\operatorname{pop}}
\newcommand{aux}[0]{\text{aux}}
\newcommand{opt}[0]{\text{opt}}
\newcommand{tgt}[0]{\text{tgt}}
\newcommand{init}[0]{\text{init}}
\newcommand{lab}[0]{\text{lab}}
\newcommand{rwa}[0]{\text{rwa}}
\newcommand{bra}[1]{\langle#1\vert}
\newcommand{ket}[1]{\vert#1\rangle}
\newcommand{Bra}[1]{\left\langle#1\right\vert}
\newcommand{Ket}[1]{\left\vert#1\right\rangle}
\newcommand{Braket}[2]{\left\langle #1\vphantom{#2}\mid{#2}\vphantom{#1}\right\rangle}
\newcommand{op}[1]{\hat{#1}}
\newcommand{Op}[1]{\hat{#1}}
\newcommand{dd}[0]{\,\text{d}}
\newcommand{Liouville}[0]{\mathcal{L}}
\newcommand{DynMap}[0]{\mathcal{E}}
\newcommand{identity}[0]{\mathbf{1}}
\newcommand{Norm}[1]{\lVert#1\rVert}
\newcommand{Abs}[1]{\left\vert#1\right\vert}
\newcommand{avg}[1]{\langle#1\rangle}
\newcommand{Avg}[1]{\left\langle#1\right\rangle}
\newcommand{AbsSq}[1]{\left\vert#1\right\vert^2}
\newcommand{Re}[0]{\operatorname{Re}}
\newcommand{Im}[0]{\operatorname{Im}}
``
"""
# ╔═╡ 03284e97-5807-4b2e-b732-5c3c25d0e300
Markdown.parse(
"""
This tutorial teaches the fundamental concepts of quantum control with `QuantumControl.jl` for the example of population transfer in a two-level system.
It uses the following package versions:
""" *
join(
[
"* `$(p[1])`: `$(p[2])`"
for p in sort(
map(
r -> (r.name, r.version),
filter(
r -> (r.is_direct_dep && !(r.name in ["Pkg"])),
collect(values(Pkg.dependencies()))
)
), by=r->r[1]
)
],
"\n"
)
)
# ╔═╡ 138c98d1-dbd5-4c81-ad86-7528eeb930d4
md"""
## Two-level Hamiltonian
We consider the Hamiltonian ``\op{H} = \op{H}_0 + \epsilon(t) \op{H}_1`` with
```math
\op{H}_{0}
= - \frac{\omega}{2} \op{\sigma}_{z}
= \begin{pmatrix} - \frac{\omega}{2} & 0 \\ 0 & \frac{\omega}{2} \end{pmatrix}\,,
```
representing a simple qubit with energy level splitting ``\omega`` in the canonical basis
```math
\ket{0} = \begin{pmatrix}1\\0\end{pmatrix},\qquad
\ket{1} = = \begin{pmatrix}0\\1\end{pmatrix}\,.
```
"""
# ╔═╡ f71c9a93-7aee-4098-acd0-1c0bdbbfe22e
function ket(label)
result = Dict(
"0" => Vector{ComplexF64}([1, 0]),
"1" => Vector{ComplexF64}([0, 1])
)
return result[string(label)]
end;
# ╔═╡ 51605cfe-80c5-40a7-be34-adb56d222791
md"""
The control field ``\epsilon(t)`` is assumed to couple via the Hamiltonian
```math
\op{H}_1
= \op{\sigma}_{x}
= \begin{pmatrix} 0 & 1 \\ 1 & 0 \end{pmatrix}
```
to the qubit, i.e., the control field effectively drives transitions between both qubit states.
We construct the full time-dependent Hamiltonian via the
"""
# ╔═╡ 3c917ae5-ae57-4ef5-8805-0d84a13aefb4
md"""
function:
"""
# ╔═╡ 553c4f5a-2fc1-432d-981e-4c4d88b7a6db
md"""
The control field here switches on from zero at ``t=0`` to it's maximum amplitude 0.2 within the time period 0.3 (the switch-on shape is half a [Blackman pulse](https://en.wikipedia.org/wiki/Window_function#Blackman_window)). It switches off again in the time period 0.3 before the final time ``T=5``). We use a time grid with 500 time steps between 0 and ``T``
"""
# ╔═╡ dea94e06-fce3-4f82-b856-17dd0e67c011
ϵ(t) = 0.2 * QuantumControl.Shapes.flattop(t, T=5, t_rise=0.3, func=:blackman);
# ╔═╡ b26a4363-411e-4de4-8585-4ec8106e0da9
function tls_hamiltonian(; ω=1.0, ϵ=ϵ)
σ̂_z = ComplexF64[
1 0
0 -1
]
σ̂_x = ComplexF64[
0 1
1 0
]
Ĥ₀ = (-ω/2) * σ̂_z
Ĥ₁ = σ̂_x
return hamiltonian(Ĥ₀, (Ĥ₁, ϵ))
end;
# ╔═╡ 9dea1166-c4a5-4ebd-95bc-bf45f3157a2d
H = tls_hamiltonian();
# ╔═╡ 93937d9e-6742-49f2-9b29-e6200db8a27d
tlist = collect(range(0, 5, length=500));
# ╔═╡ 3365b95a-6c9e-4103-8d79-d5f5f4bb317c
plot(tlist, ϵ; xlabel="time", ylabel="amplitude", label="ϵ(t)", legend=:bottom)
# ╔═╡ 0638b982-4082-4696-b7f5-27e14154e68d
md"""
## Optimization target
"""
# ╔═╡ 73ce588c-0884-45d8-88b1-4138588e6d29
md"""
The control problem is defined in terms of the "trajectory",
"""
# ╔═╡ a9b2e927-cbf0-43da-bba4-5bba9e34ae72
md"""
of the initial state ``\ket{0}`` evolving under ``\op{H}(t)``. Ideally, this evolution should reach the target state ``\ket{1}``:
"""
# ╔═╡ 32111d96-9521-4074-ac47-7a66a628b410
traj = Trajectory(initial_state=ket(0), generator=H, target_state=ket(1));
# ╔═╡ 0a72565f-351c-4617-b070-6dd4e93b2cd1
md"""
## Dynamics under the guess pulse
We can simulate the dynamics
"""
# ╔═╡ b8721ee0-d38f-4fb9-ba02-34d735626bdb
md"""
as a convenience wrapper around `QuantumPropagators.propagate`.
For a two-level system, the most efficient way to actually evaluate the propagator
``\op{U} = \exp{-i \op{H}(t) dt}`` (assumed piecewise-constant for a small timestep ``dt``) to to matrix-exponentiate the operator ``(-i \op{H}(t) dt)``, which can be done with
"""
# ╔═╡ d2968662-c4a2-4e0f-956b-554a6b0c69c4
md"""
This allows to obtain the state of the system at every point in time:
"""
# ╔═╡ 070cf752-a81c-4467-8c95-56b856631d06
states = propagate_trajectory(traj, tlist; method=ExpProp, storage=true)
# ╔═╡ 72ac090c-f2c2-43f6-a1cc-da3f229d04d0
md"""
You can see how this varies when changing the amplitude of the field ϵ(t) in the range
0 $(@bind _guess_amplitude_slider Slider(0:0.1:5, default=0.2)) 5
or by setting the value directly:
"""
# ╔═╡ 947230c5-cf7f-4d96-8ef5-ec9e153d361b
md"""
|ϵ(t)| = $(@bind _guess_amplitude_str TextField(default=string(_guess_amplitude_slider)))
"""
# ╔═╡ ea46d577-c80c-4af2-84a4-03495b231181
md"""
The dynamics of a two-level system are also analytically solvable. What we are seeing above is ["Rabi cycling"](https://en.wikipedia.org/wiki/Rabi_cycle), where the population cycles between the ``\ket{0}`` and ``\ket{1}`` state depending only on the area under the ``\epsilon(t)`` field. Since the transition is "off-resonant" with $\omega = 1 \ne 0$, the population transfer is not complete (but gets closer and closer as ``|\epsilon(t)| > \omega``).
Try to find an amplitude that gets good population transfer in the off-resonant case as well as the resonant case for ``\omega = 0``:
"""
# ╔═╡ afe49361-2cb2-4c7b-923b-251c5e90c918
md"""
ω = $(@bind _ω_str TextField(default="1.0"))
"""
# ╔═╡ beaa8b71-a44e-4196-9361-c588563cac68
begin
local _ϵ_0 = 0.2
try
_ϵ_0 = parse(Float64, _guess_amplitude_str)
catch
end
_ϵ(t) = _ϵ_0 * QuantumControl.Shapes.flattop(t, T=5, t_rise=0.3, func=:blackman);
local _ω = 1.0
try
_ω = parse(Float64, _ω_str)
catch
end
local _H = tls_hamiltonian(; ω=_ω, ϵ=_ϵ);
local _traj = Trajectory(initial_state=ket(0), generator=_H, target_state=ket(1));
local _states = propagate_trajectory(_traj, tlist; method=ExpProp, storage=true)
local _Ψ_T = _states[:,end]
local _error = 1 - abs2(_Ψ_T ⋅ ket(1))
plot(tlist, abs2.(_states)'; label=["|⟨0|Ψ(t)⟩|²" "|⟨1|Ψ(t)⟩|²"], xlabel="time", ylabel="population", title="|ϵ(t)| = $_ϵ_0; error = $(round(_error; digits=3))")
end
# ╔═╡ 18e5972c-a439-470e-bf3c-18636a2d09df
details("Solution", md"""
With the default ω = 1.0, for an amplitude of 3.0, the error is below 5%.
For a resonant field (ω = 0.0), setting |ϵ(t)| = 1/3 produces an error of zero.
""")
# ╔═╡ 4c3bc003-bcb2-43f1-b9dc-2170d5f6e9a4
md"""
## Optimization
The full control problem,
"""
# ╔═╡ d6de6da7-462f-49c2-92a9-6647aad3b4fa
md"""
includes this trajectory, information about the time grid for the dynamics, and the functional to be used:
"""
# ╔═╡ fc8e3298-4681-4202-ba57-2deab0bc3023
md"""
These are combined in
"""
# ╔═╡ b0327380-2205-49af-aeec-7529591204ef
md"""
In this case, we look at the square-modulus of the overlap with the target state.
"""
# ╔═╡ a94e69cc-ecec-427b-8791-80991e452b36
problem = ControlProblem(
trajectories=[traj],
tlist=tlist,
iter_stop=500,
prop_method=ExpProp,
J_T=J_T_sm,
check_convergence=res -> begin
((res.J_T < 1e-3) && (res.converged = true) && (res.message = "J_T < 10⁻³"))
end,
);
# ╔═╡ f8fc0c53-cc54-404f-8f29-2216cdf4427e
md"""
We solve the control problem
"""
# ╔═╡ bb48bb43-616b-4710-88e7-d667ab77fe15
md"""
which is a wrapper around `QuantumControl.optimize` that caches to the optimization result to a file. For this "trivial" example, the optimization has negligible runtime, but for other examples, an optimization might run for hours or days. Using `@optimize_or_load` ensures we don't have to re-run it, and also, that we keep an intermedite results if the optimization is interruppted.
We can use either one of the two standard gradient-based optimization methods:
"""
# ╔═╡ ce1f718d-dc55-44b2-bca4-e1b7d62f0a40
md"""
Feel free to switch between them to see what changes:
"""
# ╔═╡ 80af33a9-9637-4aae-8382-0adf551d6206
@bind method Select([GRAPE, Krotov])
# ╔═╡ b9040df9-b8c2-4008-97e8-56098d435510
opt_result = @optimize_or_load(
"opt_result_$method.jld2",
problem;
force = false, # set to `true` to overwrite existing jld2 file
method=method,
);
# ╔═╡ 379b4590-5bb9-4ee4-86e0-51e3c58831d5
opt_result
# ╔═╡ cc387f50-a707-4150-84b5-31b31141a3a2
md"""
We can extract the optimize field (an array of values) from the `opt_result`:
"""
# ╔═╡ ff9d8cb7-bbd5-446d-83bc-c56b5e6b39aa
ϵ_opt = opt_result.optimized_controls[1];
# ╔═╡ 13e23dd0-70bd-4926-b115-7fee2e349004
plot(tlist, ϵ_opt; xlabel="time", ylabel="amplitude", label="ϵ_opt(t)", legend=:bottom)
# ╔═╡ 9ffd341c-4a94-4866-b432-30a3d4662d54
md"""
If we want to see the dynamics under the optimized field,
"""
# ╔═╡ e2bfb504-c2bc-4bb6-8f8e-a40a04655163
md"""
allows to replace the original `ϵ(t)` with `ϵ_opt` inside `traj`:
"""
# ╔═╡ bd111a37-4406-4229-9000-d23dbb03b742
states_opt = propagate_trajectory(
substitute(traj, IdDict(ϵ => ϵ_opt)),
tlist;
method=ExpProp, storage=true
);
# ╔═╡ 55fee8de-854c-436c-8f35-5c8bc6e41e05
begin
local _Ψ_T = states_opt[:,end]
local _error = 1 - abs2(_Ψ_T ⋅ ket(1))
plot(tlist, abs2.(states_opt)'; label=["|⟨0|Ψ(t)⟩|²" "|⟨1|Ψ(t)⟩|²"], xlabel="time", ylabel="population", title="optimized dynamics; error = $(round(_error; digits=3))")
end
# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
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Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8"
QuantumControl = "8a270532-f23f-47a8-83a9-b33d10cad486"
QuantumPropagators = "7bf12567-5742-4b91-a078-644e72a65fc1"
[compat]
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Plots = "~1.40.16"
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QuantumControl = "~0.11.1"
QuantumPropagators = "~0.8.3"
"""
# ╔═╡ 00000000-0000-0000-0000-000000000002
PLUTO_MANIFEST_TOML_CONTENTS = """
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