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

Junxiu Liu, Tiening Sun, Yuling Luo*, Su Yang, Yi Cao, Jia Zhai

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)100-107
Number of pages8
Publication statusPublished - 2 Jan 2020
Externally publishedYes


  • Echo state network
  • Financial applications
  • Particle swarm optimization
  • Quantum computation
  • Time series


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