Experimental implementation of quantum algorithm for association rules
  mining
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 2$\times$2 transaction database (i.e., a transaction
database involving two transactions and two items), and run it on four real
five-qubit IBM quantum computers as well as on the simulator. For a larger
4$\times$4 transaction database which would lead to circuits with more qubits
and a higher depth than the currently accessible IBM real quantum devices can
handle, we also construct the quantum circuits of qARM and execute them on
"aer\_simulator" alone. Both experimental results show that all the frequent
itemsets from the two transaction databases are successfully derived as
desired, demonstrating the correctness and feasibility of qARM. Our work may
serve as a benchmarking, and provide prototypes for implementing qARM for
larger transaction databases on both noisy intermediate-scale quantum devices
and universal fault-tolerant quantum computers.
Chao-Hua Yu