Reconstructing landscapes of variational quantum algorithms (VQAs) by compressed sensing.
TODO: link our paper.
Use cases and their visualization in our paper
are generated using cs_*.py
and vis_*.ipynb
.
Commands that calling cs_*.py
to generate those use cases
are recorded in record.md.
- whether to pack up Google and IBM data, and tutorial to unzip
- pack up
figs/grid_search
andfigs/optimization
by linking Tianyi's repo - zip
figs/grid_search_recon
(too big) and put somewhere; add tutorial to unzip - sparsity data (Table IV)
- data for Fig. 12, n=20
- LICENSE
Recommend: create an Anaconda environment and install from source.
conda create -n oscar python=3.9
conda activate oscar
TODO: requirement
Download data:
sh ./download_data.sh
git clone https://github.com/kunliu7/oscar
cd oscar
pip install -e .
pytest
P.S. pytest
might takes several minutes.
TODO: link with QAOA-Simulator
.
-
cs_comp_miti.py: compare mitigated landscapes
-
cs_distributed.py: recon. distributed landscapes
-
cs_evaluate.py: compute recon. error for p=1 and p=2
-
cs_high_dim_vary_2d.py: compute recon. error for high-dim landscapes
-
cs_opt_on_recon_landscapes.py: optimize on recon. landscapes by interpolation
-
cs_second_optimize.py: second optimization proposed in paper
-
vis_OSCAR_save_queries.py: visualize #Queries saved by OSCAR