Published March 21, 2021
| Version v1
Conference paper
Open
QuClassi: A Hybrid Deep Neural Network Architecture based on Quantum State Fidelity
Creators
- 1. Pacific Northwest National Laboratory
- 2. Kent State University
- 3. Case Western Reserve University
- 4. Fordham University
- 5. University of Conneticut
Description
QuClassi: A Hybrid Deep Neural Network Architecture based on Quantum State Fidelity
MLSys 2022 Publication
QuClassi is a Quantum Deep Neural Network architecture for classification, based on quantum state fidelity
Usage
To use QuClassi, install the requirements by using
pip install -r requirements.txt
Within main.py, there is a subsampling section
SUBSAMPLE = 1000
This is to be edited according to computational constraints. More data results in slower training speeds, and hence subsamples are used for quicker evaluation.
From here, to run the system, run the command
python main.py
Subsample sets can be edited by editting the training labels and training datasets accordingly.