Pre+TTN_VQC (Pretrained Tensor-Train Network + Variational Quantum Circuit)

The package provides an implementation of Pre+TTN-VQC to corroborate our theoretical work on VQC in Refs. [1] and [2].

git clone https://github.com/uwjunqi/PreTrained-TTN_VQC
cd PreTrained-TTN_VQC

Installation

The main depencies include pytorch and torchquantum. Moreover, we need the following packages:

Our implemented package of TTN:

git clone https://github.com/uwjunqi/Pytorch-Tensor-Train-Network.git
cd Pytorch-Tensor-Train-Network
python setup.py install

Torch Quantum

pip3 install torchquantum

Experimental simulations

Running TTN-VQC

python vqc_classifier.py

Running Pre+TTN-VQC

python vqc_finetune.py

Running PCA-VQC

python pca_vqc_classifier.py

Paper Citation

If you use the codes for your research work, please consider citing the following papers:

[1] Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen, Min-Hsiu Hsieh, "Pre-Training Tensor-Train Networks Facilitate Machine Learning with Variational Quantum Circuits," arXiv:2306.03741v1, in Submission.

[2] Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen, Min-Hsiu Hsieh, "Theoretical Error Performance Analysis for Variational Quantum Circuit Based Functional Regression," Nature Publishing Group, npj Quantum Information, Vol. 9, no. 4, 2023

[3] Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen, "QTN-VQC: An End-to-End Learning Framework for Quantum Neural Networks," Physica Scripta, Vol 9, no. 1, pp. 015111, 2023.