Quantum AI simulator using a hybrid CPU-FPGA approach.

ABSTRACT: The quantum kernel method has attracted considerable attention in the field of quantum machine learning. However, exploring the applicability of quantum kernels in more realistic settings has been hindered by the number of physical qubits current noisy quantum computers have, thereby limiting the number of features encoded for quantum kernels. Hence, there is a need for an efficient, application-specific simulator for quantum computing by using classical technology. Here we focus on quantum kernels empirically designed for image classification and demonstrate a field programmable gate arrays (FPGA) implementation. We show that the quantum kernel estimation by our heterogeneous CPU-FPGA computing is 470 times faster than that by a conventional CPU implementation. The co-design of our applic

SUBMITTER: Suzuki T 

PROVIDER: S-EPMC10182082 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature