Fourier Series Guided Design of Quantum Convolutional Neural Networks for Enhanced Time Series Forecasting

arXiv:2404.15377v1 Announce Type: cross
Abstract: In this study, we apply 1D quantum convolution to address the task of time series forecasting. By encoding multiple points into the quantum circuit to predict subsequent data, each point becomes a feature, transforming the problem into a multidimensional one. Building on theoretical foundations from prior research, which demonstrated that Variational Quantum Circuits (VQCs) can be expressed as multidimensional Fourier series, we explore the capabilities of different architectures and ansatz. This analysis considers the concepts …

abstract apply arxiv building convolution convolutional neural networks cs.lg data design encoding feature forecasting fourier multidimensional multiple networks neural networks quant-ph quantum series study time series time series forecasting type