Quantum Speedup for Hypergraph Sparsification
Authors: Chenghua Liu, Minbo Gao, Zhengfeng Ji, Mingsheng Ying
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we propose the first quantum algorithm for hypergraph sparsification, addressing an open problem proposed by Apers & de Wolf (2020)... Our algorithm achieves near-linear output size matching the current best classical algorithm while operating in sublinear time e O(r mn/ε)2... The proof of correctness for the algorithm follows closely the chaining argument in Lee (2023) and Jambulapati et al. (2023). Due to space constraints, we provide the detailed proof in Appendix C. |
| Researcher Affiliation | Academia | 1Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, China 2University of Chinese Academy of Sciences, Beijing, China 3Department of Computer Science and Technology, Tsinghua University, Beijing, China 4Centre for Quantum Software and Information, University of Technology Sydney, Sydney, Australia. |
| Pseudocode | Yes | Algorithm 1 Quantum Hyperedge Leverage Score Overestimates QHLSO(OH, T, α1, α2) |
| Open Source Code | No | The paper does not contain any explicit statements about the availability of open-source code, nor does it provide links to code repositories. |
| Open Datasets | No | This paper presents a theoretical quantum algorithm for hypergraph sparsification and does not conduct experiments requiring datasets. |
| Dataset Splits | No | This paper is theoretical and does not involve empirical experiments with datasets, thus no dataset splits are discussed. |
| Hardware Specification | No | This paper describes theoretical quantum algorithms and does not report on empirical experiments or specify any hardware used for computations. |
| Software Dependencies | No | The paper describes theoretical quantum algorithms and does not mention any specific software dependencies with version numbers for implementation. |
| Experiment Setup | No | This paper focuses on theoretical algorithm design and complexity analysis, and therefore does not include details on experimental setup, hyperparameters, or training configurations. |