Quantum Boosting
Authors: Srinivasan Arunachalam, Reevu Maity
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The main contribution of this paper is a quantum algorithm that runs in time quadratically faster in O(VC(C)) to obtain a strong learner for the concept class C. |
| Researcher Affiliation | Collaboration | 1IBM Research, Yorktown Heights, USA. 2Clarendon Laboratory, University of Oxford. |
| Pseudocode | Yes | Algorithm 1 Quantum boosting algorithm |
| Open Source Code | No | The paper does not provide information about open-source code for the described methodology. There is no mention of a code repository or a statement regarding code availability. |
| Open Datasets | No | The paper is theoretical and does not use or provide access to specific publicly available datasets for empirical training. It refers to 'training set S' as a theoretical component of its algorithm. |
| Dataset Splits | No | The paper does not describe empirical experiments or dataset splits for training, validation, or testing. Its focus is on theoretical complexity and algorithm design. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe software implementations or specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and defines parameters for its algorithmic analysis (e.g., T, M, γ) rather than providing specific experimental setup details or hyperparameters for empirical evaluation. |