https://doi.org/10.1140/epjqt/s40507-025-00333-6
Research
QSegRNN: quantum segment recurrent neural network for time series forecasting
1
School of Computer Science and Engineering, Pusan National University, Busandaehak-ro 63beon-gil, Geumjeong-street, 46241, Busan, Gyeongsangnam-do, Republic of Korea
2
Department of Information Convergence Engineering, Pusan National University, Busandaehak-ro 63beon-gil, Geumjeong-street, 46241, Busan, Gyeongsangnam-do, Republic of Korea
Received:
9
January
2025
Accepted:
24
February
2025
Published online:
3
March
2025
Recently many data centers have been constructed for artificial intelligence (AI) research. The important condition of the data center is to supply sufficient electricity, resulting in many electricity transformers being installed. Especially, these electricity transformers have led to significant heat generation in many data centers. Therefore, managing the temperature of electricity transformers has emerged as an important task. Notably, numerous studies are being conducted to manage and forecast the temperature of electricity transformers using artificial intelligence models. However, as the size of predictive models increases and computational demands grow, substantial computing resources are required. Consequently, there are instances where the lack of computing resources makes these models difficult to operate. To address these challenges, we propose a quantum segment recurrent neural network (QSegRNN), a time series forecasting model utilizing quantum computing. QSegRNN leverages quantum computing to achieve comparable performance with fewer parameters than classical counterpart models under similar conditions. QSegRNN inspired by a classical SegRNN uses the quantum cell instead of the classical cell in the model. The advantage of this structure is that it can be designed with fewer parameters under similar architecture. To construct the quantum cell, we benchmark the quantum convolutional circuit with amplitude embedding as the variational quantum circuit, minimizing information loss while considering the limit of noisy intermediate-scale quantum (NISQ) devices. The experiment result illustrates that the forecasting performance of QSegRNN achieves better performance than SegRNN and other forecasting models even though QSegRNN has only 85 percent of the parameters.
Key words: Quantum–classical neural networks / Quantum encoding / Quantum machine learning / Time series forecasting / Variational quantum circuits
© The Author(s) 2025
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