README

This is the repository for the paper "Variational learning algorithms for quantum query complexity".

Currently, this repo contains the following files:

1. /VarQQA folder : the main file for the paper, contains the implementation of the variational learning algorithms for quantum query complexity.
2. /sdp folder: contains the SDP solver used in the paper.
3. /data folder: contains the quantum query algorithm found the by VarQQA in this paper.It contains three subfolders
   1. /data/grover: contains the data for Grover search
   2. /data/hamming_weight_modulo: contains the data for the Hamming Weight Modulo
   3. /data/EXACT: contains the data for the $\mathrm{EXACT}_{k,l}^n$ function

To reproduce the ressult, create a new python environment with the following command:

conda create -y -n cuda118
conda install -y -n cuda118 -c conda-forge pytorch ipython matplotlib scipy tqdm cvxpy
conda activate cuda118

Run the corresponding python file in the /VarQQA for the VarQQA algorithm.