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..
Near-Optimal Quantum Coreset Construction Algorithms for Clustering
Authors: Yecheng Xue, Xiaoyu Chen, Tongyang Li, Shaofeng H.-C. Jiang
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We give quantum algorithms that find coresets for kclustering in Rd with O(nkd3/2) query complexity. Our coreset reduces the input size from n to poly(kε 1d), so that existing α-approximation algorithms for clustering can run on top of it and yield (1 + ε)α-approximation. This eventually yields a quadratic speedup for various kclustering approximation algorithms. We complement our algorithm with a nearly matching lower bound, that any quantum algorithm must make Ω(nk) queries in order to achieve even O(1)-approximation for k-clustering. |
| Researcher Affiliation | Academia | 1Center on Frontiers of Computing Studies, Peking University, Beijing, China 2School of Electronics Engineering and Computer Science, Peking University, Beijing, China. |
| Pseudocode | Yes | Algorithm 1 Bicriteria Approximation, Algorithm 2 Coreset Construction, Algorithm 3 Multidimensional Quantum Counting |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or provide links to a code repository for the described methodology. |
| Open Datasets | No | The paper is theoretical, describing algorithms and proving lower bounds for k-clustering in Rd, and does not mention specific datasets used for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments involving dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, thus there are no hardware specifications for running experiments. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithm design and proofs, and therefore does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments, therefore it does not provide details about an experimental setup, hyperparameters, or training configurations. |