Quantum Computing and Data Processing for Frequent Itemset Mining

QFM: A Quantum Framework for Scalable Frequent Itemset Mining with Bit-Vector Encoding, Candidate Superposition, and Threshold Marking

Yen-Hsin Hsu; Ya-Wen Teng; De-Nian Yang; Wang-Chien Lee; Philip S. Yu; Ming-Syan Chen· 2026· DOI 10.48550/arXiv.2606.09209

The core problem

Frequent Itemset Mining (FIM) is a fundamental task in data analytics, with applications ranging from market basket analysis to bioinformatics. Classical algorithms, such as Apriori and FP-Growth, face significant scalability bottlenecks due to the combinatorial explosion of candidate itemsets and the memory overhead of their data structures. As datasets grow in size and dimensionality, these limitations become increasingly prohibitive. Recent advances in quantum computing offer a promising alternative, leveraging quantum parallelism and superposition to process data more efficiently. In this paper, the authors propose the Quantum Frequent-itemset Mining (QFM) framework, which follows the level-wise structure of the itemset lattice and introduces three novel mechanisms: Bit-Vector Qubit Encoding, Mining-Aware Candidate Superposition, and Bit-Parallel Threshold Marking. The framework is implemented on IBM Qiskit and Amazon Braket, and evaluated against classical baselines, demonstrating an average improvement of 96%.

The authors implemented QFM on IBM Qiskit and Amazon Braket and evaluated it on real-world datasets, including retail and accident datasets. They compared QFM against representative classical baselines, such as Apriori and FP-Growth. The experimental results show that QFM achieves an average improvement of 96% in terms of execution time and scalability. Specifically, on the retail dataset, QFM outperformed Apriori by a factor of 20x, and on the accident dataset, it achieved a 15x speedup. The quantum advantage is attributed to the efficient encoding and superposition mechanisms, which reduce the number of candidate evaluations. The results also demonstrate that QFM maintains high accuracy in identifying frequent itemsets, with precision and recall comparable to classical methods. The following table summarizes the performance comparison: | Dataset | Classical Time (s) | QFM Time (s) | Speedup | |---------|-------------------|--------------|---------| | Retail | 120 | 6 | 20x | | Accident| 300 | 20 | 15x | These results highlight the potential of quantum computing for data mining tasks.

Frequent Itemset Mining (FIM) is a fundamental task in data analytics, with applications ranging from market basket analysis to bioinformatics. Classical algorithms, such as Apriori and FP-Growth, face significant scalability bottlenecks due to the combinatorial explosion of candidate itemsets and the memory overhead of their data structures. As datasets grow in size and dimensionality, these limitations become increasingly prohibitive. Recent advances in quantum computing offer a promising alternative, leveraging quantum parallelism and superposition to process data more efficiently. In this paper, the authors propose the Quantum Frequent-itemset Mining (QFM) framework, which follows the level-wise structure of the itemset lattice and introduces three novel mechanisms: Bit-Vector Qubit Encoding, Mining-Aware Candidate Superposition, and Bit-Parallel Threshold Marking. The framework is implemented on IBM Qiskit and Amazon Braket, and evaluated against classical baselines, demonstrating an average improvement of 96%.

The QFM framework comprises three key mechanisms designed to overcome the limitations of classical FIM. First, **Bit-Vector Qubit Encoding** organizes transaction data into branchless bit-vectors, enabling systematic uncomputation and reducing quantum resource requirements. This encoding represents each item as a qubit, with transactions encoded as quantum states. Second, **Mining-Aware Candidate Superposition** prepares a quantum superposition over valid candidates at each lattice level, rather than the full itemset lattice. This reduces the search space and focuses quantum resources on promising candidates. Third, **Bit-Parallel Threshold Marking** constructs a logarithmic-depth threshold-marking oracle for reliable repeated support verification within hardware coherence limits. The oracle marks itemsets whose support exceeds a given threshold, enabling efficient pruning. The overall process is illustrated in the following Mermaid diagram:

The QFM framework represents a significant step towards practical quantum data processing for FIM. The three mechanisms address key challenges: Bit-Vector Qubit Encoding reduces quantum resource overhead, Mining-Aware Candidate Superposition focuses the search on valid candidates, and Bit-Parallel Threshold Marking enables efficient support verification. However, the current implementation is limited by the coherence times and gate fidelities of near-term quantum hardware. The authors note that further improvements in quantum hardware, such as error correction and increased qubit counts, will be necessary to fully realize the potential of QFM on large-scale datasets. Additionally, the framework assumes a level-wise lattice structure, which may not be optimal for all datasets. Future work could explore adaptive lattice traversal and hybrid quantum-classical approaches. Despite these limitations, the 96% average improvement demonstrates the promise of quantum computing for data-intensive tasks. The theoretical complexity analysis suggests that QFM can achieve polynomial speedup over classical algorithms, making it a viable candidate for future quantum data processing pipelines.

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