Efficient Quantum Approximate kNN Algorithm via Granular-Ball Computing

Authors: Shuyin Xia, Xiaojiang Tian, Suzhen Yuan, Jeremiah D. Deng

IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical A comprehensive time complexity analysis reveals that the proposed algorithm achieves a lower time complexity than existing classical and quantum k NN algorithms.
Researcher Affiliation Academia 1School of Artificial Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China 2School of Electronic Science and Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China 3Key Laboratory of Big Data Intelligent Computing, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China 4School of Computing, University of Otago, Dunedin 9054, New Zealand EMAIL, EMAIL, EMAIL, EMAIL
Pseudocode Yes Algorithm 1 Similarity Calculation Algorithm 2 Quantum Compare Algorithm 3 Quantum HNSW Construction Algorithm 4 Quantum HNSW Search
Open Source Code No The paper does not provide any concrete access to source code, such as a specific repository link, an explicit code release statement, or code in supplementary materials for the methodology described.
Open Datasets No The paper mentions "large amounts of data" and "large-scale data sets" in a general context but does not specify any particular datasets used for experiments, nor does it provide links or citations for any.
Dataset Splits No The paper does not provide specific dataset split information, as it does not present experimental results on specific datasets.
Hardware Specification No The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. The paper focuses on theoretical complexity analysis.
Software Dependencies No The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. The paper focuses on theoretical algorithm design and complexity analysis.
Experiment Setup No The paper does not contain specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) in the main text. The paper primarily focuses on theoretical analysis of algorithm complexity.