Data-Driven Acceleration of Quantum Optimization and Machine Learning via Koopman Operator Learning

Abstract

Efficient optimization methods play a crucial role for quantum optimization and machine learning on near-term quantum computers. Unlike classical computers, obtaining gradients on quantum computers is costly with sample complexity scaling with the number of parameters and measurements. In this paper, we connect the natural gradient method in quantum optimization with Koopman operator theory, which provides a powerful framework for predicting nonlinear dynamics. We propose a data-driven approach for accelerating quantum optimization and machine learning via Koopman operator learning. To predict parameter updates on quantum computers, we develop new methods including the sliding window dynamic mode decomposition (DMD) and the neural-network-based DMD. We apply our methods both on simulations and real quantum hardware. We demonstrate efficient prediction and acceleration of gradient optimization on the variational quantum eigensolver and quantum machine learning.