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 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.

arXiv comment32 pages, 4 figures, 8 supplementary figures, 1 supplementary table

References in corpus (27)

• Array Programming with NumPy
• Quantum Computing in the NISQ era and beyond
• Quantum Machine Learning
• Supervised learning with quantum enhanced feature spaces
• Quantum support vector machine for big data classification
• Noisy intermediate-scale quantum (NISQ) algorithms
• Quantum machine learning in feature Hilbert spaces
• Quantum Circuit Learning
• Quantum Chemistry in the Age of Quantum Computing
• Parameterized quantum circuits as machine learning models
• Power of data in quantum machine learning
• A rigorous and robust quantum speed-up in supervised machine learning
• Qulacs: a fast and versatile quantum circuit simulator for research purpose
• Demonstration of fault-tolerant universal quantum gate operations
• Classical simulation of commuting quantum computations implies collapse of the polynomial hierarchy
• Quantum Risk Analysis
• Quantum machine learning beyond kernel methods
• Training Quantum Embedding Kernels on Near-Term Quantum Computers
• Qibo: a framework for quantum simulation with hardware acceleration
• General-purpose quantum circuit simulator with Projected Entangled-Pair States and the quantum supremacy frontier
• Automatic design of quantum feature maps
• Experimental quantum kernel machine learning with nuclear spins in a solid
• Jet: Fast quantum circuit simulations with parallel task-based tensor-network contraction
• Predicting toxicity by quantum machine learning
• A quantum circuit simulator and its applications on Sunway TaihuLight supercomputer
• Application of quantum computing to a linear non-Gaussian acyclic model for novel medical knowledge discovery
• Error mitigation for quantum kernel based machine learning methods on IonQ and IBM quantum computers