Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Quantum Boosting
Authors: Srinivasan Arunachalam, Reevu Maity
ICML 2020 | Venue PDF | 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. |