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}
}