Frequent Itemset Mining with Quantum Computing

2026-06-08

AI Summary

This work addresses the challenges of candidate explosion and high support-maintenance overhead in frequent itemset mining on dense datasets by proposing QFM, a quantum frequent itemset mining framework that deeply integrates quantum superposition and parallelism into the mining process. QFM employs three core mechanisms—bit-vector-based qubit encoding, mining-aware candidate superposition generation, and bit-parallel threshold labeling—to efficiently verify itemset supports. Theoretical analysis demonstrates that the proposed approach significantly reduces both time and space complexity compared to classical methods. Experimental evaluations conducted on IBM Qiskit and Amazon Braket confirm that QFM outperforms existing classical baselines in terms of performance, validating its practical potential for accelerating frequent pattern discovery in dense transactional data.

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.

Problem

• Frequent Itemset Mining
• candidate space explosion
• support counting
• dense datasets
• computational bottleneck

Innovation

• Quantum Computing
• Frequent Itemset Mining
• Qubit Encoding
• Candidate Superposition
• Bit-Parallel Threshold Marking