Belkadi, Widad HassinaDrias, YassineDrias, Habiba2025-11-052025-11-0520261568-49461872-9681https://doi.org/10.1016/j.asoc.2025.114011This study introduces a novel quantum version of the FP-growth algorithm for frequent itemset mining, leveraging the combined strengths of classical FP-growth and quantum machine learning. Key contributions include the theoretical and practical framework for Quantum FP-growth, along with a comprehensive analysis of its time and space complexity. We implemented Quantum FP-growth using IBM Qiskit and conducted a comparative evaluation of various quantum amplitude estimation (QAE) methods, including Canonical QAE, Faster QAE, Maximum Likelihood QAE, and Iterative QAE for support estimation. Our findings reveal that Iterative QAE surpasses the other methods in both accuracy and speed. Additionally, we explored the advantages of GPU simulation with IBM Qiskit and NVIDIA cuQuantum. Notably, this research marks the first application of a quantum frequent itemset mining algorithm to a real-world dataset in Digital Soil Mapping (DSM), pioneering the use of quantum technologies in soil science. This study underscores the potential of quantum computing to revolutionize data mining and promote sustainable soil management practices.eninfo:eu-repo/semantics/closedAccessQuantum Machine LearningQuantum FP-GrowthDigital Soil MappingGPU SimulationQuantum Amplitude EstimationQuantum Amplitude AmplificationcuQuantumArticle10.1016/j.asoc.2025.1140112-s2.0-105019097704