The application of near-term quantum devices to machine learning (ML) has attracted much attention. In one such attempt, Mitarai et al. (2018) proposed a framework to use a quantum circuit for supervised ML tasks, which is called quantum circuit learning (QCL). Due to the use of a quantum circuit, QCL can employ an exponentially high-dimensional Hilbert space as its feature space. However, its efficiency compared to classical algorithms remains unexplored. In this study, using a statistical technique called count sketch, we propose a classical ML algorithm that uses the same Hilbert space. In numerical simulations, our proposed algorithm demonstrates similar performance to QCL for several ML tasks. This provides a new perspective with which to consider the computational and memory efficiency of quantum ML algorithms.
@article{arxiv.2003.10667,
title = {Quantum circuit-like learning: A fast and scalable classical machine-learning algorithm with similar performance to quantum circuit learning},
author = {Naoko Koide-Majima and Kei Majima},
journal= {arXiv preprint arXiv:2003.10667},
year = {2021}
}