Emulation of high-performance correlation-based quantum clustering algorithm for two-dimensional data on FPGA

Abstract

Clustering algorithms are used to classify the unlabeled data into a number of categories with polynomial time complexity. Quantum clustering algorithms are developed to improve the performance and to achieve higher gain. In this work, we implement the quantum k-means clustering algorithm on field-programmable gate array (FPGA) by exploiting the implicit parallelism of the FPGA technology to achieve high speed among the software-implemented recent proposals. To do that, we establish a new method to measure the inner product between two qubits which is based on the correlation between the Euclidean distance and the inner product. We also optimize the quantum gates in terms of speed and removing the discretization error. Experimental results show a reduction in the running time by 500× as compared to the classical k-means algorithm for the A1 standard dataset.