Repository for Robust Quantum Optimal Control with Trajectory Optimization
This repository is associated with the paper Robust Quantum Optimal Control with Trajectory Optimization, a collaboration between Schuster Lab and the Robotic Exploration Lab. This repo contains the files and information necessary to reproduce the work.
This repo is NOT a package. The optimization problems are defined using TrajectoryOptimization.jl and RobotDynamics.jl. The optimization problems are solved with Altro.jl. For those familiar with the quantum optimal control (QOC) literature, ALTRO is a solver in the same sense that GOAT, GRAPE, and Krotov are solvers. This repo is merely a set of files to demonstrate how to use ALTRO for QOC, and in particular, how to engineer robustness to parameter uncertainties and mitigate decoherence using the techniques we introduced in the paper.
This repo will NOT be updated to reflect new versions of its dependencies. However, this repo will be updated for clarity. If you feel that an aspect of the documentation for this work is lacking, e.g. a part of this README is ambiguous or a file could be better commented / explained, or you find a bug, please file a GitHub issue. Other inquiries about this work can be directed to Thomas Propson or David Schuster.
To execute the code in this repo, you will need to install Julia.
Julia is a dynamically-typed and just-in-time (JIT) compiled programming language designed for high performance computing. Julia is similar to Python in terms of the easy-to-read syntax you have come to love, but dissimilar in terms of the slow for-loops you have come to not so love. Julia provides substantial performance benefits for this work through compiler optimizations, most importantly those in StaticArrays.jl. We encourage the interested reader to check out the links in the Related Work section to find out more about Julia's performance benefits.
Julia uses a different package-management scheme than Python.
With Python, you use third-party installers like pip or conda
to manage your global environment.
With Julia, you use the Pkg
module from the standard library
to manage an environment for each project. This is similar to the
concept of a pipfile. The packages used by this project
are defined in Manifest.toml and Project.toml
at the top level of the repo.
First, clone the repo.
$ git clone https://github.com/SchusterLab/rbqoc.git
Navigate to the top level.
$ cd rbqoc
Enter the Julia read-eval-print-loop (REPL).
$ julia
Import the Pkg module.
julia> using Pkg
Activate the project.
julia> Pkg.activate(".")
Instantiate the project.
julia> Pkg.instantiate()
You have now downloaded all of the necessary packages.
The base optimization outlined in section III of the paper
is a good starting point. It can be found in src/spin/spin13.jl.
Navigate to the file.
$ cd src/spin
Enter the Julia REPL.
$ julia
Include the file. If this is your first time including the file, all of the dependent packages will be precompiled. Precompiling will take some time, but it will only happen once.
julia> include("spin13.jl")
Run the optimization. All of the optimizations in this
repo are called with a function named run_traj. The hyperparameters
and output of the optimization can be modified by passing
arguments to this function, see the corresponding
file
for details.
juila> run_traj()
src/rbqoc.jl and src/spin/spin.jl contain common definitions.
The analytic pulses were generated with src/spin/spin14.py.
In Figure 1, the depolarization aware pulses were generated with src/spin/spin15.jl
In Figure 2, the sampling method corresponds to src/spin/spin12.jl, the unscented
sampling method corresponds to src/spin/spin30.jl, and the derivative methods
correspond to src/spin/spin11.jl.
In Figure 3, the sampling method corresponds to src/spin/spin18.jl,
the unscented sampling method corresponds to src/spin/spin25.jl,
and the derivative methods correspond to src/spin/spin17.jl.
The data for the figures in the paper can be produced with src/spin/figures.jl
and the figures can be produced with src/spin/figures.py. In src/spin/figures.jl
you will find references to HDF5 files with the name structure XXXXX_spinYY.h5.
These files are output by each of the optimization programs named src/spin/spinYY.jl
and contain the optimized pulse. The HDF5 files used for the paper are
available upon request from the authors--we did not put them in the repo because
they are large binary files--but they can
be generated on your machine by running the corresponding optimization program
src/spin/spinYY.jl with the hyperparameters listed in nb/trials.xlsx, or
its Google Sheet counterpart
trials.xlsx.
- Trajectory Optimization
- Julia
- QOC
- c3 Toolset for control, calibration and characterization of physical systems
- CRAB CRAB in MATLAB
- Dynamo DYNAMO in MATLAB
- GOAT-QuantumControl GOAT in MATLAB
- JuliaQuantumControl The JuliaQuantumControl organization collects packages implementing a comprehensive collection of methods of open-loop quantum optimal control
- Juqbox.jl QOC from LLNL in Julia, symplectic integrators, pseudo-spectral, exact derivatives
- krotov Krotov in Python
- python-open-controls QOC from Q-CTRL in Python
- qoc GRAPE via autograd in Python
- qopt and filter-functions QOC with filter functions
- quantum-optimal-control GRAPE via TensorFlow in Python
- QuTiP QOC with QuTip
@article{propson2022robust,
title = {Robust Quantum Optimal Control with Trajectory Optimization},
author = {Propson, Thomas and Jackson, Brian E. and Koch, Jens and Manchester, Zachary and Schuster, David I.},
journal = {Phys. Rev. Applied},
volume = {17},
issue = {1},
pages = {014036},
numpages = {15},
year = {2022},
month = {Jan},
publisher = {American Physical Society},
doi = {10.1103/PhysRevApplied.17.014036},
url = {https://link.aps.org/doi/10.1103/PhysRevApplied.17.014036}
}