Frequent Itemset Mining with Quantum Computing
2026-06-08 • Databases
Databases
AI summaryⓘ
The authors address a common data analysis problem called Frequent Itemset Mining, which becomes very slow and complex when working with large, dense datasets. They suggest using quantum computing to improve how data candidates are represented and checked, proposing a new system called QuantumFreqMine (QFM). This system uses three main ideas involving quantum bits to speed up the process. The authors tested QFM on real quantum computing platforms and found it works better than existing methods. They also provide a detailed analysis of its performance and resource needs.
Frequent Itemset MiningQuantum ComputingQubit EncodingCandidate SuperpositionThreshold MarkingQuantum AlgorithmsIBM QiskitAmazon BraketTime ComplexityData Analytics
Authors
Yen-Hsin Hsu, Ya-Wen Teng, De-Nian Yang, Wang-Chien Lee, Philip S. Yu, Ming-Syan Chen
Abstract
Frequent Itemset Mining (FIM) is a foundational task in data analytics, but its candidate and conditional pattern spaces can grow rapidly, and maintaining support information becomes increasingly costly on dense datasets. These bottlenecks present a critical opportunity for quantum computing to redesign the way candidate representation and support verification are organized. Motivated by recent developments in quantum computing, we propose the \textit{QuantumFreqMine (QFM)} framework for FIM. QFM introduces three mechanisms: (1)~\textit{Bit-Vector Qubit Encoding}, (2)~\textit{Mining-Aware Candidate Superposition}, and (3)~\textit{Bit-Parallel Threshold Marking}. We provide a theoretical analysis in terms of time complexity, space comlexity, and logical resource usage. We implement QFM on IBM Qiskit and Amazon Braket. The experiments demonstrate that QFM outperforms representative baselines.