Private learning implies quantum stability
Authors: Yihui Quek, Srinivasan Arunachalam, John A Smolin
NeurIPS 2021 | Conference 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 Srinivasan.Arunachalam@ibm.com Yihui Quek Information Systems Laboratory, Stanford University, USA yquek@stanford.edu John Smolin IBM Quantum, IBM T.J. Watson Research Center, Yorktown Heights, USA Smolin@us.ibm.com |
| 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. |