https://arxiv.org/abs/2312.16985
Project description
qsensoropt
qsensoropt
is a library based on Tensorflow 2 for the automatic optimization of adaptive and non-adaptive controls in quantum metrology tasks.
Docs
Documentation for this project is available on gitlab pages.
Along with the description of all the modules, the documentation contains many examples of use on which the user can base new applications.
Installation
The following instructions are written for a Linux system.
It is advisable to install qsensoropt
in a conda environment created for the purpose.
-
If conda is not available on you machine, install it with the following commands
curl https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -o Miniconda3-latest-Linux-x86_64.sh bash Miniconda3-latest-Linux-x86_64.sh
-
Resource the bash configuration with
source ~/.bashrc
-
Create a new conda environment named
qsensoropt
conda create --name qsensoropt python>=3.10
-
Activate the environment just created and upgrade pip
conda activate qsensoropt pip install --upgrade pip
-
qsensoropt
is based on Machine Learning and a such it is best used with a GPU. If a GPU is available on your system you can use it by installing CUDA and cuDNNconda install -c conda-forge cudatoolkit=11.8 pip install nvidia-cudnn-cu11==8.6
Configure the system path with
mkdir -p $CONDA_PREFIX/etc/conda/activate.d echo 'CUDNN_PATH=$(dirname $(python -c "import nvidia.cudnn;print(nvidia.cudnn.__file__)"))' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh echo 'export LD_LIBRARY_PATH=$CONDA_PREFIX/lib/:$CUDNN_PATH/lib:$LD_LIBRARY_PATH' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
and restart the environment with
conda deactivate conda activate qsensoropt
-
Clone the repository and enter in the
qsensoropt
directorygit clone https://gitlab.com/federico.belliardo/qsensoropt.git cd qsensoropt
-
Install the
qsensoropt
library and all its dependenciespip install -e .
Congratulation! You can now optimize your quantum sensor!
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