An echo state network architecture based on quantum logic gate and its optimization

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Liu, Junxiu, Sun, Tiening, Luo, Yuling, Yang, Su ORCID: https://orcid.org/0000-0002-6618-7483, Cao, Yi and Zhai, Jia (2020) An echo state network architecture based on quantum logic gate and its optimization. Neurocomputing, 371. pp. 100-107.

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Official URL: https://doi.org/10.1016/j.neucom.2019.09.002

Abstract

Quantum neural network (QNN) is developed based on two classical theories of quantum computation and artificial neural networks. It has been proved that quantum computing is an important candidate for improving the performance of traditional neural networks. In this work, inspired by the QNN, the quantum computation method is combined with the echo state networks (ESNs), and a hybrid model namely quantum echo state network (QESN) is proposed. Firstly, the input training data is converted to quantum state, and the internal neurons in the dynamic reservoir of ESN are replaced by qubit neurons. Then in order to maintain the stability of QESN, the particle swarm optimization (PSO) is applied to the model for the parameter optimizations. The synthetic time series and real financial application datasets (Standard & Poor's 500 index and foreign exchange) are used for performance evaluations, where the ESN, autoregressive integrated moving average (ARIMAX) are used as the benchmarks. Results show that the proposed PSO-QESN model achieves a good performance for the time series predication tasks and is better than the benchmarking algorithms. Thus, it is feasible to apply quantum computing to the ESN model, which provides a novel method to improve the ESN performance.

Item Type:	Article
Identifier:	10.1016/j.neucom.2019.09.002
Keywords:	Quantum computation, Echo state network, Particle swarm optimization, Time series, Financial applications
Subjects:	Computing
Related URLs:	Publisher
Depositing User:	Su Yang
Date Deposited:	03 Jun 2021 10:43
Last Modified:	04 Nov 2024 11:45
URI:	https://repository.uwl.ac.uk/id/eprint/7924