Quantum AI simulator using a hybrid CPU-FPGA approach

TL;DR

This paper presents a hybrid CPU-FPGA simulator for quantum kernels in machine learning, achieving significant speedups and enabling large-scale simulations for image classification tasks.

Contribution

It introduces an application-specific FPGA implementation of quantum kernels, significantly accelerating quantum kernel estimation and allowing large feature simulations.

Findings

Quantum kernel estimation is 470 times faster with FPGA.

Able to simulate up to 780-dimensional features.

Quantum kernel performs comparably to Gaussian kernels in classification.

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 application-specific quantum kernel and its efficient FPGA implementation enabled us to perform one of the largest numerical simulations of a gate-based quantum kernel in terms of features, up to 780-dimensional features. We apply our quantum kernel to classification tasks using Fashion-MNIST dataset and show that our quantum kernel is comparable to Gaussian kernels with the optimized hyperparameter.