QAS-bench: rethinking quantum architecture search and a benchmark

Datasets

You can download the dataset provided in this paper at this link. The format of the dataset is as follows:

.
├── task1
│   ├── datamatrix
│   └── figure
└── task2
    ├── 2bit_arbit
    ├── 3bit_arbit
    ├── 4bit_arbit
    └── 5bit_arbit

Baselines

We provide our source code for task1 and task2, containing our designed search algorithm, simulated annealing and genetic algorithm.

For task1, the necessary inputs are qubit_num, layer_num, matrix_path, output_path.

The code can be called as follows:

python task1/Genetic/Genetic.py --qubit_num 2 --layer_num 4 --matrix_path data/task1/1bit/matrix_dense1_task0.pkl --output_path result.pkl

python task1/Search/Search.py --qubit_num 2 --layer_num 4 --matrix_path data/task1/1bit/matrix_dense1_task0.pkl --output_path result.pkl

python task1/Anneal/Anneal.py --qubit_num 2 --layer_num 4 --matrix_path data/task1/1bit/matrix_dense1_task0.pkl --output_path result.pkl

The circuits and calculated losses will be saved in result.pkl.

For Task2, the necessary inputs are qubit_num, layer_num, matrix_path, output_path.

The code can be called as follows:

python task2/Search/Search.py --qubit_num 3 --layer_num 10 --matrix_path data/task2/3bit_arbit/0/0_train.pkl --output_path result.pkl --valid_path data/task2/3bit_arbit/0/0_valid.pkl 

python task2/Genetic/Genetic.py --qubit_num 3 --layer_num 10 --matrix_path data/task2/3bit_arbit/0/0_train.pkl --output_path result.pkl --valid_path data/task2/3bit_arbit/0/0_valid.pkl 

python task2/Anneal/Anneal.py --qubit_num 3 --layer_num 10 --matrix_path data/task2/3bit_arbit/0/0_train.pkl --output_path result.pkl --valid_path data/task2/3bit_arbit/0/0_valid.pkl 

The circuits and calculated losses will be saved in result.pkl.

Other baselines

Reinforcement Learning method is based on this link.

Hybrid Algorithm is based on this link.

Differentiable Algorithm is based on this link.

Citation

@inproceedings{lu2023qas,
  title={QAS-bench: rethinking quantum architecture search and a benchmark},
  author={Lu, Xudong and Pan, Kaisen and Yan, Ge and Shan, Jiaming and Wu, Wenjie and Yan, Junchi},
  booktitle={International Conference on Machine Learning},
  pages={22880--22898},
  year={2023},
  organization={PMLR}
}