Quantum Ai Simulator Using A Hybrid Cpu-fpga Approach

The quantum kernel method is an important algorithm in quantum machine learning techniques that can be used for binary and multiclass classification. It is based on a quantum feature map that is used to estimate the kernel matrix. The training phase is performed on classical computers, while the quantum kernel entries are computed by NISQ computers or quantum computing simulators. This paper describes a quantum AI simulator using a heterogeneous CPU-FPGA computing and provides an overview of the quantum kernel method.