Quantum Lower Bounds for Finding Stationary Points of Nonconvex Functions
Authors: Chenyi Zhang, Tongyang Li
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we conduct a systematic study of quantum lower bounds on finding ϵapproximate stationary points of nonconvex functions |
| Researcher Affiliation | Academia | 1Computer Science Department, Stanford University 2Institute for Interdisciplinary Information Sciences, Tsinghua University 3Center on Frontiers of Computing Studies, Peking University 4School of Computer Science, Peking University. |
| Pseudocode | No | The paper describes theoretical concepts and proofs, but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper focuses on theoretical lower bounds and does not mention releasing any source code for the described work. |
| Open Datasets | No | This is a theoretical paper focusing on lower bounds and proofs. It does not involve experimental training with datasets. |
| Dataset Splits | No | This is a theoretical paper focusing on lower bounds and proofs. It does not involve experimental validation with datasets. |
| Hardware Specification | No | As a theoretical paper, no experiments requiring specific hardware were conducted, and thus, no hardware specifications are mentioned. |
| Software Dependencies | No | As a theoretical paper, no experiments requiring specific software dependencies with version numbers were conducted. |
| Experiment Setup | No | As a theoretical paper, no experiments were set up, and therefore, no details such as hyperparameters or training settings are provided. |