  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.
