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..
Gibbs Sampling of Continuous Potentials on a Quantum Computer
Authors: Arsalan Motamedi, Pooya Ronagh
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Additionally, as concrete numerical demonstrations, Figs. 2 and 3 showcase the error of our interpolation approach applied on the functions considered in Examples A.2 and A.3. |
| Researcher Affiliation | Collaboration | 1Institute for Quantum Computing, University of Waterloo, Waterloo, ON, Canada 2Department of Physics & Astronomy, University of Waterloo, Waterloo, ON, Canada 3Perimeter Institute for Theoretical Physics, Waterloo, ON, Canada 4Irreversible, Vancouver, BC, Canada. |
| Pseudocode | Yes | Algorithm 1 Pseudocode of our Gibbs sampling algorithm. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | No | The paper does not refer to any specific publicly available datasets with concrete access information for training or evaluation. It discusses theoretical properties of functions and uses |
| Dataset Splits | No | The paper does not specify any dataset splits (training, validation, test) as it focuses on theoretical algorithm development and complexity analysis, not empirical evaluation on specific datasets with standard splits. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. It is a theoretical paper focusing on quantum algorithms and complexity analysis. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper discusses theoretical parameters (e.g., semi-analyticity parameters C and a, N, M, T as inputs to Algorithm 1) relevant to its mathematical framework and complexity analysis. However, it does not provide specific experimental setup details like hyperparameters, optimizers, or system-level training settings as typically found in empirical machine learning papers. |