A study on quantum reservoir recurrent models for time-constrained volatile sequence forecasting

This paper investigates the potential of quantum-enhanced models for time series forecasting in environments where time is a fundamental constraint. The focus is on on the quantum long short term memory networks with reservoir (QR-LSTM), and our proposed novel extension, the Autoencoded Quantum Reservoir LSTM Model (QR-LSTM-AE). The QR-LSTM, while offering substantial computational benefits in terms of time and resource efficiency, incurs a slight decrease in performance relative to the fully trainable quantum LSTM model. To address this trade-off, we introduce the QR-LSTM-AE, which utilizes an autoencoder-based strategy to recover performance lost in the reservoir alone, achieving superior accuracy without the need for full retraining. This approach not only maintains the efficiency of the QR-LSTM but also significantly reduces the computational cost associated with adapting to new data or time series, making it ideal for real-time forecasting applications. The experiments are carried out on a energy-related, highly volatile dataset, and results underscore the importance of balancing predictive accuracy with computational efficiency, highlighting the potential of quantum models to offer practical solutions in time-constrained environments. Our findings demonstrate that the QR-LSTM-AE effectively balances predictive accuracy and computational efficiency, paving the way for future advancements in quantum-enhanced forecasting through self-adaptive models, quantum autoencoders, and attention-based reservoir computing.