Experimental implementation of quantum algorithm for association rules mining
Chao-Hua Yu

TL;DR
This paper demonstrates the experimental implementation of a quantum algorithm for association rules mining (qARM) on real quantum computers and simulators, confirming its correctness and feasibility for small and larger databases.
Contribution
It provides the first experimental realization of qARM on actual quantum hardware and simulators, validating its potential for future larger-scale data mining tasks.
Findings
Successfully derived frequent itemsets from small transaction databases
Validated the correctness of qARM on real quantum devices and simulators
Showed feasibility of scaling qARM to larger databases with current technology
Abstract
Recently, a quantum algorithm for a fundamentally important task in data mining, association rules mining (ARM), called qARM for short, has been proposed. Notably, qARM achieves significant speedup over its classical counterpart for implementing the main task of ARM, i.e., finding frequent itemsets from a transaction database. In this paper, we experimentally implement qARM on both real quantum computers and a quantum computing simulator via the IBM quantum computing platform. In the first place, we design quantum circuits of qARM for a 22 transaction database (i.e., a transaction database involving two transactions and two items), and run it on four real five-qubit IBM quantum computers as well as on the simulator. For a larger 44 transaction database which would lead to circuits with more qubits and a higher depth than the currently accessible IBM real quantum devices…
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