Performance of Qubit Neural Network in Chaotic Time Series Forecasting
In recent years, quantum inspired neural networks have been applied to various practical problems since their proposal. Here we investigate whether our qubit neural network(QNN) leads to an advantage over the conventional (real-valued) neural network(NN) in the forecasting of chaotic time series. QNN is constructed from a set of qubit neuron, of which internal state is a coherent superposition of qubit states. In this paper, we evaluate the performance of QNN through a prediction of well-known Lorentz attractor, which produces chaotic time series by three dynamical systems. The experimental results show that QNN can forecast time series more precisely, compared with the conventional NN. In addition, we found that QNN outperforms the conventional NN by reconstructing the trajectories of Lorentz attractor.
KeywordsQuantum information processing Qubit Neural network Chaotic time series forecasting
This study was financially supported by Japan Society for the Promotion of Science (Scientific Research (C) 16K00337).
- 3.Perus, M.: Neuro-quantum parallelism in brain-mind and computers. Informatica (Ljubljana) 20(2), 173–184 (1996)Google Scholar
- 9.Matsui, N., Nishimura, H., Isokawa, T.: Qubit neural networks: its performance and applications. In: Nitta, T. (ed.) Complex-Valued Neural Networks: Utilizing High-Dimensional Parameters, chap. XIII, pp. 325–351. Information Science Reference, Hershey, New York (2009)Google Scholar
- 10.Kido, K.: Short-term prediction on chaotic time-series using neurocomputing. In: Proceedings of the 1st Western Pacific and 3rd Australia-Japan Workshop, pp. 278–284 (1999)Google Scholar