Introducing CoQS-LSTM, a Coherent Quantum Long Short-Term Memory Architecture. This article explores how quantum machine learning can enhance classical time-series prediction. By integrating Parameterized Quantum Circuits into LSTM networks, the authors propose a hybrid model that leverages quantum coherence to capture complex temporal dependencies. The approach is benchmarked against classical LSTMs on synthetic and real-world datasets, including electricity consumption forecasting. Results show that CoQS-LSTM can achieve competitive or superior performance with fewer parameters, suggesting a promising direction for quantum-enhanced sequence modeling. The discussion covers limitations, such as noise sensitivity and hardware constraints, and outlines future research directions, including scaling to larger datasets and exploring alternative quantum architectures. This work represents a step toward practical quantum advantage in time-sensitive predictive tasks.
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