Code accompanying the paper "ReCon: Reconfiguring Analog Rydberg Atom Quantum Computers for Quantum Generative Adversarial Networks" https://doi.org/10.1145/3676536.3676697
The implementation code is written in the Julia language (version 1.10.2).
You can run the code to train a GAN for a single class of a data set by running the following in your terminal:
julia --project=./juliaNeutralAtom rydbergEnsemble.jl $DATASET $CLASSNUM $RABIPULSE $DETUNEPULSE
where $DATASET is the dataset to be used (normally MNIST or FashionMNIST), $CLASSNUM is an integer [0, 9] representing the digit/clothing class, $RABIPULSE is the pulse function/shape to be used for the Rabi frequency, and $DETUNE is the pulse function/shape to be used for the detuning. The available pulse functions are specified in utils/pulseLibrary.jl. Note that there are hardware constraints on which pulses can be used; these are explained in the paper.
For convenience, you can run the shell script train.sh, which will execute the above for all classes in a given dataset.
The first time you run this, you may be prompted to download the MNIST or FashionMNIST datasets.
You can run the code to plot images and compute evaluation metrics by running the file ensemblePlotting.jl . To save time, this script uses previously computed and saved PCA feature weights, so that it does not need to rerun the Hamiltonian simulation each time. These weights are saved in the folder generatedPCs. If you would like to regenerate the PCA features instead, you can do so by setting dataIsSaved = false.
Real hardware runs are done on the Aquila computer from QuEra, which we accessed using the python interface for Amazon Braket. The code used to run this is in ReConAquila.ipynb. The parameters used for each run are saved in the folder pythonInput. The python requirements for this notebook can be found in python_requirements.txt
Simulation and evaluation code written in Julia v1.10.2. All package data is stored within the juliaNeutralAtom folder.
Code for interfacing with Aquila via Braket written in python 3.12.1. Package requirements are stored in python_requirements.txt
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