This paper aims at solving time series prediction problems by means of a hybrid quantum-classical recurrent neural network. We propose a novel architecture based on stacked Long Short-Term Memory layers and a variational quantum layer. The latter employs a quantum feature map to embed input data into quantum states, which are then processed by a circuit ansatz. Finally, the expectation value of the circuit's outcome is taken over Pauli observables. Quantum properties such as superposition and entanglement are exploited to perform computations efficiently in a high-dimensional feature space. The proposed hybrid quantum-classical neural network is applied to a real-life challenging problem pertaining to the prediction of renewable energy time series. The comparison between the proposed approach and the classical counterpart shows that the former achieves better results in terms of prediction error, thus demonstrating better approximation of stochastic fluctuations and an overall effectiveness of the quantum variational approach also for prediction tasks.

Hybrid Quantum-Classical Recurrent Neural Networks for Time Series Prediction / Ceschini, A.; Rosato, A.; Panella, M.. - 2022-:(2022), pp. 1-8. (Intervento presentato al convegno 2022 International Joint Conference on Neural Networks, IJCNN 2022 tenutosi a Padova, Italy) [10.1109/IJCNN55064.2022.9892441].

Hybrid Quantum-Classical Recurrent Neural Networks for Time Series Prediction

Ceschini A.;Rosato A.;Panella M.
2022

Abstract

This paper aims at solving time series prediction problems by means of a hybrid quantum-classical recurrent neural network. We propose a novel architecture based on stacked Long Short-Term Memory layers and a variational quantum layer. The latter employs a quantum feature map to embed input data into quantum states, which are then processed by a circuit ansatz. Finally, the expectation value of the circuit's outcome is taken over Pauli observables. Quantum properties such as superposition and entanglement are exploited to perform computations efficiently in a high-dimensional feature space. The proposed hybrid quantum-classical neural network is applied to a real-life challenging problem pertaining to the prediction of renewable energy time series. The comparison between the proposed approach and the classical counterpart shows that the former achieves better results in terms of prediction error, thus demonstrating better approximation of stochastic fluctuations and an overall effectiveness of the quantum variational approach also for prediction tasks.
2022
2022 International Joint Conference on Neural Networks, IJCNN 2022
hybrid quantum-classical neural networks; recurrent neural networks; time series prediction
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Hybrid Quantum-Classical Recurrent Neural Networks for Time Series Prediction / Ceschini, A.; Rosato, A.; Panella, M.. - 2022-:(2022), pp. 1-8. (Intervento presentato al convegno 2022 International Joint Conference on Neural Networks, IJCNN 2022 tenutosi a Padova, Italy) [10.1109/IJCNN55064.2022.9892441].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1658031
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