{"type":"ScholarlyArticle","title":"Efficient Quantum Approximate kNN Algorithm via Granular-Ball Computing","authors":[{"name":"Shuyin Xia","slug":"shuyin-xia","given":"Shuyin","family":"Xia","affiliation":""},{"name":"Xiaojiang Tian","slug":"xiaojiang-tian","given":"Xiaojiang","family":"Tian","affiliation":""},{"name":"Suzhen Yuan","slug":"suzhen-yuan","given":"Suzhen","family":"Yuan","affiliation":""},{"name":"Jeremiah D. Deng","slug":"jeremiah-d-deng","given":"Jeremiah D.","family":"Deng","affiliation":""}],"abstract":"High time complexity is one of the biggest challenges faced by k-Nearest Neighbors (kNN). Although current classical and quantum kNN algorithms have made some improvements, they still have a speed bottleneck when facing large amounts of data. To address this issue, we propose an innovative algorithm called Granular-Ball based Quantum kNN(GB-QkNN). This approach achieves higher efficiency by first employing granular-balls, which reduces the data size needed to processed. The search process is then accelerated by adopting a Hierarchical Navigable Small World (HNSW) method. Moreover, we optimize the time-consuming steps, such as distance calculation, of the HNSW via quantization, further reducing the time complexity of the construct and search process. By combining the use of granular-balls and quantization of the HNSW method, our approach manages to take advantage of these treatments and significantly reduces the time complexity of the kNN-like algorithms, as revealed by a comprehensive complexity analysis.","doi":"10.24963/ijcai.2025/739","url":"https://ijcai.org/proceedings/2025/739","pdf":"https://ijcai.org/proceedings/2025/0739.pdf","markdown":"https://ijcai.org/proceedings/2025/739.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/739","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Machine Learning","track":"Main Track","number":739,"pages":{"first":6642,"last":6649},"keywords":["Search: S: Search and machine learning","Machine Learning: ML: Clustering","Machine Learning: ML: Other","Machine Learning: ML: Classification"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}