Gibbs Sampling of Continuous Potentials on a Quantum Computer
Authors: Arsalan Motamedi, Pooya Ronagh
ICML 2024 | Conference PDF | Archive PDF | Plain Text | 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. |