Conference paper Open Access

# QuClassi: A Hybrid Deep Neural Network Architecture based on Quantum State Fidelity

Samuel A. Stein; Betis Baheri; Daniel Chen; Ying Mao; Qiang Guan; Shuai Xu; Caiwen Ding; Ang Li

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.

Files (14.0 kB)
Name Size
main.py
md5:eebde725c0574f283c77565a43ae2266
13.2 kB
md5:0e43899cb15b00c8a297bc079395789c
823 Bytes
requirements.txt
md5:59b869ff15852bb1d3a3d0c78534ab2f
57 Bytes
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