VSQL: Variational Shadow Quantum Learning for Classification
Authors: Guangxi Li, Zhixin Song, Xin Wang8357-8365
AAAI 2021 | Venue 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 EMAIL, EMAIL, EMAIL |
| 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. |