Multivariate time series prediction based on quantum enhanced LSTM models

Diankang Li

doi:10.1117/12.2685468

15 August 2023 Multivariate time series prediction based on quantum enhanced LSTM models

Diankang Li

Proceedings Volume 12719, Second International Conference on Electronic Information Technology (EIT 2023); 1271920 (2023) https://doi.org/10.1117/12.2685468
Event: Second International Conference on Electronic Information Technology (EIT 2023), 2023, Wuhan, China

Abstract

Long short-term memory (LSTM) is a widely used artificial neural network that is well suited for time series prediction. Quantum machine learning as a new research topic combines the advantages of quantum data processing and classical machine learning. In this paper, based on a hybrid quantum classical scheme, we design a quantum enhanced LSTM model and several variants such as QGRU. We also performed experiments with a multivariate time series prediction problem to verify the feasibility of these models. Through this research, we expect to explore the benefits and implementation of quantum-based machine learning.

Citation Download Citation

Diankang Li "Multivariate time series prediction based on quantum enhanced LSTM models", Proc. SPIE 12719, Second International Conference on Electronic Information Technology (EIT 2023), 1271920 (15 August 2023); https://doi.org/10.1117/12.2685468

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available