Private learning implies quantum stability
Authors: Yihui Quek, Srinivasan Arunachalam, John A Smolin
NeurIPS 2021 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This is a theoretical paper with no societal impacts. |
| Researcher Affiliation | Collaboration | Srinivasan Arunachalam IBM Quantum, IBM T.J. Watson Research Center, Yorktown Heights, USA EMAIL Yihui Quek Information Systems Laboratory, Stanford University, USA EMAIL John Smolin IBM Quantum, IBM T.J. Watson Research Center, Yorktown Heights, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 Robust Standard Optimal Algorithm |
| Open Source Code | No | The paper states "N/A" for including code or data. There is no mention of open-source code for the methodology described. |
| Open Datasets | No | The paper states "N/A" for running experiments. It is a theoretical paper and does not involve the use of datasets for training. |
| Dataset Splits | No | The paper states "N/A" for running experiments. It is a theoretical paper and does not involve dataset splits for validation. |
| Hardware Specification | No | The paper states "N/A" for running experiments. It is a theoretical paper and does not specify any hardware used for experiments. |
| Software Dependencies | No | The paper states "N/A" for running experiments. It is a theoretical paper and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper states "N/A" for running experiments. It is a theoretical paper and does not provide details about an experimental setup. |