Implementation of Quantum Random Access Coding (QRAC) and Trainable Embedding (TE) based on our preprinted paper here

To run the experiment provided in the paper, please take a look at configs/expriment_config.yaml. In experiment_config.yaml, the structure is

<dataset 1 (exp_name)>
    - <method 1 (sub_exp_name)>
        - param 1
        - param 2
    - <method 2>
        - param 1
        - param 2

<dataset 2>
...

which there are 2 levels, exp_name and sub_exp_name. To run all expriment in the dataset, please run the python script run_exp.py with the following argument

python run_exp --exp_name <dataset_name>

For example, to run all breast cancer dataset experiments

python run_exp --exp_name bc

If you want to run a single experiment, please specify the sub_exp_name. For example, we run the QRAC method on breast cancer.

python run_exp.py --exp_name bc --sub_exp_name qrac

Ther are 4 files for different experiment

parity_check.py - Parity function problem
bc.py - Breast cancer
titanic.py - Titanic Survival
mnist.py - MNIST handwritten digit

The parameter for each experiment can be listed using --help.

6 bits parity check

run_exp.py --exp_name pc_6 --sub_exp_name te_pc_real_device

Please update detail at this line

100 epochs Titanic Survival

run_exp.py --exp_name ts --sub_exp_name te_reg_ts_real

Please update detail at this line

You can change the parameters in config/experiment_config.yaml.

@article{Thumwanit2021TrainableDF,
  title={Trainable Discrete Feature Embeddings for Variational Quantum Classifier},
  author={Napat Thumwanit and Chayaphol Lortararprasert and Hiroshi Yano and Raymond H. Putra},
  journal={ArXiv},
  year={2021},
  volume={abs/2106.09415}
}