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.

翻译：频繁项集挖掘（FIM）是数据分析中的基础性任务，但其候选模式空间和条件模式空间会迅速增长，且在稠密数据集上维护支持度信息的成本日益高昂。这些瓶颈为量子计算提供了重新设计候选表示与支持度验证组织方式的关键契机。受近期量子计算发展的启发，我们提出了用于FIM的\textit{QuantumFreqMine（QFM）}框架。QFM引入三种机制：（1）\textit{比特向量量子比特编码}，（2）\textit{挖掘感知候选叠加}，（3）\textit{比特并行阈值标记}。我们从时间复杂度、空间复杂度和逻辑资源使用方面进行了理论分析，并在IBM Qiskit和Amazon Braket上实现了QFM。实验表明，QFM性能优于代表性基线方法。