all AI news
Fourier Series Guided Design of Quantum Convolutional Neural Networks for Enhanced Time Series Forecasting
April 25, 2024, 7:42 p.m. | Sandra Leticia Ju\'arez Osorio, Mayra Alejandra Rivera Ruiz, Andres Mendez-Vazquez, Eduardo Rodriguez-Tello
cs.LG updates on arXiv.org arxiv.org
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
More from arxiv.org / cs.LG updates on arXiv.org
Sliced Wasserstein with Random-Path Projecting Directions
1 day, 11 hours ago |
arxiv.org
Learning Extrinsic Dexterity with Parameterized Manipulation Primitives
1 day, 11 hours ago |
arxiv.org
The Un-Kidnappable Robot: Acoustic Localization of Sneaking People
1 day, 11 hours ago |
arxiv.org
Jobs in AI, ML, Big Data
Data Engineer
@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania
Artificial Intelligence – Bioinformatic Expert
@ University of Texas Medical Branch | Galveston, TX
Lead Developer (AI)
@ Cere Network | San Francisco, US
Research Engineer
@ Allora Labs | Remote
Ecosystem Manager
@ Allora Labs | Remote
Founding AI Engineer, Agents
@ Occam AI | New York