VSQL: Variational Shadow Quantum Learning for Classification
Authors: Guangxi Li, Zhixin Song, Xin Wang8357-8365
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate the efficiency of VSQL in quantum classification via numerical experiments on the classification of quantum states and the recognition of multi-labeled handwritten digits. |
| Researcher Affiliation | Collaboration | Guangxi Li,1,2 Zhixin Song,1 Xin Wang1 1Institute for Quantum Computing, Baidu Research, Beijing 100193, China 2Centre for Quantum Software and Information, University of Technology Sydney, NSW 2007, Australia guangxi.li@student.uts.edu.au, zhixinsong0524@gmail.com, wangxin73@baidu.com |
| Pseudocode | Yes | Algorithm 1 Variational shadow quantum learning (VSQL) for binary classification: the training process |
| Open Source Code | No | The paper mentions |
| Open Datasets | Yes | MNIST (Le Cun et al. 1998) |
| Dataset Splits | Yes | Then, we randomly select 80% of them as the training set and the rest 20% as the validation set. |
| Hardware Specification | No | The paper does not specify the hardware used for simulations, such as CPU or GPU models. |
| Software Dependencies | No | All the simulations and optimization loop are implemented via Paddle Quantum2 on the Paddle Paddle Deep Learning Platform (Ma et al. 2019). |
| Experiment Setup | Yes | During the optimization loop, we choose the Adam (Kingma and Ba 2015) optimizer with a learning rate LR = 0.03. ... During the optimization, we choose the Adam optimizer with a batch size of 20 samples and a learning rate of LR = 0.02. ... All the other settings are identical to the binary case, except for a new batch size of 200 samples. |