Quantum Wasserstein Generative Adversarial Networks
Authors: Shouvanik Chakrabarti, Huang Yiming, Tongyang Li, Soheil Feizi, Xiaodi Wu
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our numerical study, via classical simulation of quantum systems, shows the more robust and scalable numerical performance of our quantum WGANs over other quantum GAN proposals. In Section 5, we supplement our theoretical results with experimental validations via classical simulation of q WGAN. |
| Researcher Affiliation | Academia | 1 Joint Center for Quantum Information and Computer Science, University of Maryland 2 Department of Computer Science, University of Maryland 3 School of Information and Software Engineering University of Electronic Science and Technology of China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks that are clearly labeled as such. |
| Open Source Code | Yes | All source codes are publicly available at https://github.com/yiminghwang/qWGAN. |
| Open Datasets | No | The paper's experiments use quantum states (e.g., 1, 2, 4, 8 qubit pure states, mixed quantum states) that are generated for the purpose of the experiment rather than being derived from a publicly available or open dataset with concrete access information. |
| Dataset Splits | No | The paper mentions evaluating performance through "average fidelity for 10 runs with random initializations" but does not provide specific dataset split information (e.g., percentages or sample counts) for training, validation, or testing. |
| Hardware Specification | Yes | Most of the simulations were run on a dual core Intel I5 processor with 8G memory. The 8-qubit pure state case was run on a Dual Intel Xeon E5-2697 v2 @ 2.70GHz processor with 128G memory. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions that "Our quantum WGAN is trained using the alternating gradient descent method" and refers to "parameter choices" being in Supplemental Materials D, but it does not include concrete hyperparameter values or detailed system-level training settings in the main text. |