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 Divide-and-Conquer Anchoring for Separable Non-negative Matrix Factorization
Authors: Yuxuan Du, Tongliang Liu, Yinan Li, Runyao Duan, Dacheng Tao
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper investigates how the power of quantum computation can be capitalized to solve the non-negative matrix factorization with the separability assumption (SNMF) by devising a quantum algorithm based on the divide-and-conquer anchoring (DCA) scheme [Zhou et al., 2013]. The design of quantum DCA (QDCA) is challenging. In the divide step, the random projections in DCA is completed by a quantum algorithm for linear operations, which achieves the exponential speedup. We then devise a heuristic post-selection procedure which extracts the information of anchors stored in the quantum states efficiently. Under a plausible assumption, QDCA performs efficiently, achieves the quantum speedup, and is beneficial for high dimensional problems. |
| Researcher Affiliation | Collaboration | Yuxuan Du1 , Tongliang Liu1 , Yinan Li2 , Runyao Duan2,3 , Dacheng Tao1 1 UBTECH Sydney AI Centre, SIT, FEIT, University of Sydney, Australia 2 Centre for Quantum Software and Information, University of Technology Sydney 3 Institute for Quantum Computing, Baidu Inc., Beijing 100193, China |
| Pseudocode | Yes | Algorithm 1 summarizes the proposed QDCA algorithm. |
| Open Source Code | No | The paper does not provide any information about open-source code availability, such as a link to a repository or a statement of code release. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and complexity. It does not conduct experiments on specific datasets, so there is no mention of dataset access or public availability. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments with data. Therefore, it does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper focuses on theoretical algorithm design for quantum computing and does not describe any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and describes an algorithm. It does not provide details on specific software dependencies or version numbers. |
| Experiment Setup | No | The paper discusses a theoretical quantum algorithm and its complexity. It does not provide details on an experimental setup, such as hyperparameters or system-level training settings. |